AI research papers - TechTalks https://bdtechtalks.com Technology solving problems... and creating new ones Mon, 07 Apr 2025 13:15:08 +0000 en-US hourly 1 https://i0.wp.com/bdtechtalks.com/wp-content/uploads/2018/02/cropped-TechTalks-logo.jpg?fit=32%2C32&ssl=1 AI research papers - TechTalks https://bdtechtalks.com 32 32 99082954 Under the hood: The Innovations powering DeepSeek’s AI breakthrough https://bdtechtalks.com/2025/04/07/deepseek-innovations/?utm_source=rss&utm_medium=rss&utm_campaign=deepseek-innovations https://bdtechtalks.com/2025/04/07/deepseek-innovations/#respond Mon, 07 Apr 2025 13:00:00 +0000 https://bdtechtalks.com/?p=24275 Here is how DeepSeek models disrupted AI norms and revealed that outstanding performance and efficiency don’t require secrecy

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How Open-Sora 2.0 cuts the costs of AI video generation without sacrificing quality https://bdtechtalks.com/2025/03/24/open-sora-2/?utm_source=rss&utm_medium=rss&utm_campaign=open-sora-2 https://bdtechtalks.com/2025/03/24/open-sora-2/#respond Mon, 24 Mar 2025 14:11:14 +0000 https://bdtechtalks.com/?p=24158 Open-Sora 2.0 cuts the costs of creating a bleeding edge text-to-video AI model by using the right data, architecture, and training regime.

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Claude 3.5 Sonnet outperforms GPT-4o and o1 in software engineering, OpenAI study shows https://bdtechtalks.com/2025/02/24/claude-3-5-sonnet-outperforms-gpt-4o-and-o1-in-software-engineering-openai-study-shows/?utm_source=rss&utm_medium=rss&utm_campaign=claude-3-5-sonnet-outperforms-gpt-4o-and-o1-in-software-engineering-openai-study-shows https://bdtechtalks.com/2025/02/24/claude-3-5-sonnet-outperforms-gpt-4o-and-o1-in-software-engineering-openai-study-shows/#respond Mon, 24 Feb 2025 14:00:00 +0000 https://bdtechtalks.com/?p=23928 A new OpenAI study reveals Claude 3.5 Sonnet outperforms GPT-4o and o1 on SWE-Lancer, a new benchmark simulating real-world software engineering tasks.

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How multiagent fine-tuning overcomes the data bottleneck of LLMs https://bdtechtalks.com/2025/01/27/llm-multiagent-fine-tuning/?utm_source=rss&utm_medium=rss&utm_campaign=llm-multiagent-fine-tuning https://bdtechtalks.com/2025/01/27/llm-multiagent-fine-tuning/#respond Mon, 27 Jan 2025 17:03:28 +0000 https://bdtechtalks.com/?p=23636 Multiagent debate and fine-tuning can enable LLMs to create high-quality training data to improve themselves across different tasks.

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New training paradigm prevents machine learning models from learning spurious correlations https://bdtechtalks.com/2025/01/20/memorization-aware-training-machine-learning/?utm_source=rss&utm_medium=rss&utm_campaign=memorization-aware-training-machine-learning https://bdtechtalks.com/2025/01/20/memorization-aware-training-machine-learning/#respond Mon, 20 Jan 2025 14:26:35 +0000 https://bdtechtalks.com/?p=23563 Meta researchers show how memorization-aware training can help machine learning models avoid developing dangerous biases.

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GEAR turbo-charges LLMs with advanced graph-based RAG capabilities https://bdtechtalks.com/2025/01/13/gear-graph-based-llm-rag/?utm_source=rss&utm_medium=rss&utm_campaign=gear-graph-based-llm-rag https://bdtechtalks.com/2025/01/13/gear-graph-based-llm-rag/#respond Mon, 13 Jan 2025 20:44:56 +0000 https://bdtechtalks.com/?p=23513 GEAR enhances RAG by automatically extracting triples and using beam search to create and iterate over graph representations from retrieved documents.

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Augmentation-based jailbreaking reveals critical flaws in AI models https://bdtechtalks.com/2024/12/30/best-of-n-jailbreaking/?utm_source=rss&utm_medium=rss&utm_campaign=best-of-n-jailbreaking https://bdtechtalks.com/2024/12/30/best-of-n-jailbreaking/#respond Mon, 30 Dec 2024 14:00:00 +0000 https://bdtechtalks.com/?p=23371 Best-of-N jailbreaking is a black-box attack that can circumvent the safeguards of frontier LLMs, including Claude, GPT-4o, and Gemini.

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Encoders make a strong comeback with ModernBERT https://bdtechtalks.com/2024/12/27/modernbert-llm-encoder/?utm_source=rss&utm_medium=rss&utm_campaign=modernbert-llm-encoder https://bdtechtalks.com/2024/12/27/modernbert-llm-encoder/#respond Fri, 27 Dec 2024 14:15:52 +0000 https://bdtechtalks.com/?p=23343 ModernBERT combines the powers of encoder-based models with the latest techniques in making transformers more efficient.

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Tokenformer is a Transformer model that scales more efficiently https://bdtechtalks.com/2024/12/16/tokenformer-model-transformer-alternative/?utm_source=rss&utm_medium=rss&utm_campaign=tokenformer-model-transformer-alternative https://bdtechtalks.com/2024/12/16/tokenformer-model-transformer-alternative/#respond Mon, 16 Dec 2024 14:00:00 +0000 https://bdtechtalks.com/?p=23214 Tokenformer uses the attention mechanism exclusively to create a transformer architecture that can be scaled without training from scratch.

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LLMs don’t need all the attention layers, study shows https://bdtechtalks.com/2024/12/09/llm-attention-layer-pruning/?utm_source=rss&utm_medium=rss&utm_campaign=llm-attention-layer-pruning https://bdtechtalks.com/2024/12/09/llm-attention-layer-pruning/#respond Mon, 09 Dec 2024 14:00:00 +0000 https://bdtechtalks.com/?p=23145 LLMs can shed a substantial portion of their attention layers without hurting their performance.

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Nvidia’s Hymba is an efficient SLM that combines state-space models and transformers https://bdtechtalks.com/2024/12/02/nvidia-hymba-slm/?utm_source=rss&utm_medium=rss&utm_campaign=nvidia-hymba-slm https://bdtechtalks.com/2024/12/02/nvidia-hymba-slm/#respond Mon, 02 Dec 2024 13:58:52 +0000 https://bdtechtalks.com/?p=23072 Hymba integrates transformers and state-space models to reduce costs and increase speed while maintaining accuracy.

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How treating LLMs as “actors” can produce better results https://bdtechtalks.com/2024/11/25/llm-method-actors/?utm_source=rss&utm_medium=rss&utm_campaign=llm-method-actors https://bdtechtalks.com/2024/11/25/llm-method-actors/#respond Mon, 25 Nov 2024 13:56:08 +0000 https://bdtechtalks.com/?p=22989 Think of LLMs as actors, prompts as scripts, and LLM outputs as performances.

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Self-Evolving Reward Learning aligns LLMs with less human feedback https://bdtechtalks.com/2024/11/18/self-evolving-reward-learning-aligns-llms-with-less-human-feedback/?utm_source=rss&utm_medium=rss&utm_campaign=self-evolving-reward-learning-aligns-llms-with-less-human-feedback https://bdtechtalks.com/2024/11/18/self-evolving-reward-learning-aligns-llms-with-less-human-feedback/#respond Mon, 18 Nov 2024 12:50:59 +0000 https://bdtechtalks.com/?p=22926 Large language models (LLMs) have internal world models that they can use to review their own answers and automatically label data to train reward models.

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Adversarial pop-ups trick AI agents into clicking malicious links https://bdtechtalks.com/2024/11/10/adversarial-popups-ai-agents/?utm_source=rss&utm_medium=rss&utm_campaign=adversarial-popups-ai-agents https://bdtechtalks.com/2024/11/10/adversarial-popups-ai-agents/#respond Sun, 10 Nov 2024 21:34:26 +0000 https://bdtechtalks.com/?p=22847 AI agents click on malicious popups that human users would easily avoid.

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New technique teaches LLMs to optimize their “thought” process https://bdtechtalks.com/2024/11/04/thinking-llms/?utm_source=rss&utm_medium=rss&utm_campaign=thinking-llms https://bdtechtalks.com/2024/11/04/thinking-llms/#respond Mon, 04 Nov 2024 13:59:40 +0000 https://bdtechtalks.com/?p=22774 Though Preference Optimization (TPO) teaches LLMs to generate logical thoughts before responding to queries.

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Minimized RNNs offer a fast and efficient alternative to Transformers https://bdtechtalks.com/2024/10/28/minimized-rnn-vs-transformer/?utm_source=rss&utm_medium=rss&utm_campaign=minimized-rnn-vs-transformer https://bdtechtalks.com/2024/10/28/minimized-rnn-vs-transformer/#respond Mon, 28 Oct 2024 14:08:42 +0000 https://bdtechtalks.com/?p=22698 With a few changes, RNNs can be optimized for parallel training, making them competitive with Transformers while keeping them efficient.

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Would you play an AI-generated game? https://bdtechtalks.com/2024/10/25/unbounded-ai-generated-game/?utm_source=rss&utm_medium=rss&utm_campaign=unbounded-ai-generated-game https://bdtechtalks.com/2024/10/25/unbounded-ai-generated-game/#respond Fri, 25 Oct 2024 20:33:56 +0000 https://bdtechtalks.com/?p=22661 Unbounded is a game engine that creates interactive experiences on the fly using LLMs and image generation models.

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The (not so) hidden costs of AI’s “Bigger is Better” paradigm https://bdtechtalks.com/2024/10/20/costs-ai-bigger-is-better/?utm_source=rss&utm_medium=rss&utm_campaign=costs-ai-bigger-is-better https://bdtechtalks.com/2024/10/20/costs-ai-bigger-is-better/#respond Sun, 20 Oct 2024 20:29:04 +0000 https://bdtechtalks.com/?p=22617 The arms race for scaling AI models comes at the cost of less efficient solutions, narrow research directions, and centralization of power.

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Simulating millions of LLM agents with AgentTorch https://bdtechtalks.com/2024/10/02/agenttorch-llm-agents/?utm_source=rss&utm_medium=rss&utm_campaign=agenttorch-llm-agents https://bdtechtalks.com/2024/10/02/agenttorch-llm-agents/#respond Wed, 02 Oct 2024 19:06:20 +0000 https://bdtechtalks.com/?p=22489 AgentTorch is a framework that allows you to simulate large populations through LLM agents and archetypes.

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Promtriever trains LLMs for information retrieval and instruction following https://bdtechtalks.com/2024/09/23/promptriever-llm-information-retrieval/?utm_source=rss&utm_medium=rss&utm_campaign=promptriever-llm-information-retrieval https://bdtechtalks.com/2024/09/23/promptriever-llm-information-retrieval/#respond Mon, 23 Sep 2024 12:51:14 +0000 https://bdtechtalks.com/?p=22424 Information retrieval should not come at the cost of instruction-following capabilities.

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Can AI make scientific discoveries? https://bdtechtalks.com/2024/09/16/can-ai-make-scientific-discoveries/?utm_source=rss&utm_medium=rss&utm_campaign=can-ai-make-scientific-discoveries https://bdtechtalks.com/2024/09/16/can-ai-make-scientific-discoveries/#respond Mon, 16 Sep 2024 08:30:50 +0000 https://bdtechtalks.com/?p=22358 Current AI algorithms can solve the "easy problem" of scientific research, but the "hard problem" of coming up with the actual problem is the human's job.

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How LLMs can automatically design agentic systems https://bdtechtalks.com/2024/09/09/adas-automated-agent-design/?utm_source=rss&utm_medium=rss&utm_campaign=adas-automated-agent-design https://bdtechtalks.com/2024/09/09/adas-automated-agent-design/#respond Mon, 09 Sep 2024 13:51:34 +0000 https://bdtechtalks.com/?p=22315 Why not let LLMs design agentic system themselves? This is what ADAS proposes.

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What to know about GameNGen, Google’s DOOM simulator https://bdtechtalks.com/2024/09/02/google-gamengen-doom-simulator/?utm_source=rss&utm_medium=rss&utm_campaign=google-gamengen-doom-simulator https://bdtechtalks.com/2024/09/02/google-gamengen-doom-simulator/#respond Mon, 02 Sep 2024 09:05:01 +0000 https://bdtechtalks.com/?p=22251 Google Research's GameNGen is a diffusion model that can imagine DOOM video frames. Why would we need such a thing?

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How UC Berkeley is making humanoid robotic research fast and affordable https://bdtechtalks.com/2024/08/19/berkeley-humanoid-robot/?utm_source=rss&utm_medium=rss&utm_campaign=berkeley-humanoid-robot https://bdtechtalks.com/2024/08/19/berkeley-humanoid-robot/#respond Mon, 19 Aug 2024 12:36:12 +0000 https://bdtechtalks.com/?p=22141 Researchers at UC Berkeley have released a mid-sized humanoid robot that is safe, affordable, and has a thin sim-to-real gap.

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Thinking in graphs improves LLMs’ planning abilities, but challenges remain https://bdtechtalks.com/2024/08/12/thinking-in-graphs-improves-llms-planning-abilities-but-challenges-remain/?utm_source=rss&utm_medium=rss&utm_campaign=thinking-in-graphs-improves-llms-planning-abilities-but-challenges-remain https://bdtechtalks.com/2024/08/12/thinking-in-graphs-improves-llms-planning-abilities-but-challenges-remain/#respond Mon, 12 Aug 2024 08:23:08 +0000 https://bdtechtalks.com/?p=22087 LLMs perform very poorly at planning asynchronous task. But formulating the task as a graph can help improve their performance.

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Why accuracy is a misleading metric when evaluating compressed LLMs https://bdtechtalks.com/2024/08/06/why-accuracy-is-a-misleading-metric-when-evaluating-compressed-llms/?utm_source=rss&utm_medium=rss&utm_campaign=why-accuracy-is-a-misleading-metric-when-evaluating-compressed-llms https://bdtechtalks.com/2024/08/06/why-accuracy-is-a-misleading-metric-when-evaluating-compressed-llms/#respond Tue, 06 Aug 2024 20:06:49 +0000 https://bdtechtalks.com/?p=22047 Compressed LLMs maintain their accuracy metrics in comparison to the baseline models. But their behavior changes dramatically, according to other metrics.

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Meta SAM 2 is the most impressive object segmentation model https://bdtechtalks.com/2024/08/05/meta-sam-2-object-segmentation-model/?utm_source=rss&utm_medium=rss&utm_campaign=meta-sam-2-object-segmentation-model https://bdtechtalks.com/2024/08/05/meta-sam-2-object-segmentation-model/#respond Mon, 05 Aug 2024 07:05:33 +0000 https://bdtechtalks.com/?p=22021 Meta's new object segmentation model, SAM 2, provides near-real-time inference on a wide variety of objects and environments.

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Why vision-language models fail on simple visual tests https://bdtechtalks.com/2024/08/01/vlms-visual-test-failures/?utm_source=rss&utm_medium=rss&utm_campaign=vlms-visual-test-failures https://bdtechtalks.com/2024/08/01/vlms-visual-test-failures/#respond Thu, 01 Aug 2024 14:14:56 +0000 https://bdtechtalks.com/?p=22000 Vision-language models (VLMs) score high on competitive multi-modal benchmarks but fail on basic visual acuity tests, according to a new study.

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How to turbocharge LLMs for spreadsheet tasks https://bdtechtalks.com/2024/07/29/microsoft-spreadsheetllm/?utm_source=rss&utm_medium=rss&utm_campaign=microsoft-spreadsheetllm https://bdtechtalks.com/2024/07/29/microsoft-spreadsheetllm/#respond Mon, 29 Jul 2024 07:48:34 +0000 https://bdtechtalks.com/?p=21961 Large language models are not designed for spreadsheets. Microsoft's SpreadsheetLLM makes spreadsheets digestible by LLMs.

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PAS finds the best prompting technique for your LLM https://bdtechtalks.com/2024/07/22/pas-automatic-prompt-engineering-llms/?utm_source=rss&utm_medium=rss&utm_campaign=pas-automatic-prompt-engineering-llms https://bdtechtalks.com/2024/07/22/pas-automatic-prompt-engineering-llms/#respond Mon, 22 Jul 2024 07:26:20 +0000 https://bdtechtalks.com/?p=21909 PAS is an automated prompt engineering (APE) system that chooses the best prompting technique for each input to an LLM.

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How AI agents can self-improve with symbolic learning https://bdtechtalks.com/2024/07/08/ai-agent-symbolic-learning/?utm_source=rss&utm_medium=rss&utm_campaign=ai-agent-symbolic-learning https://bdtechtalks.com/2024/07/08/ai-agent-symbolic-learning/#respond Mon, 08 Jul 2024 08:19:19 +0000 https://bdtechtalks.com/?p=21821 Researchers at AIWaves have released a symbolic learning framework that allows LLM-based AI agents to self-improve their components based on new data.

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DeepMind releases benchmark for evaluating long-context LLMs https://bdtechtalks.com/2024/07/01/deepmind-loft-long-context-llm/?utm_source=rss&utm_medium=rss&utm_campaign=deepmind-loft-long-context-llm https://bdtechtalks.com/2024/07/01/deepmind-loft-long-context-llm/#respond Mon, 01 Jul 2024 07:50:18 +0000 https://bdtechtalks.com/?p=21783 Google DeepMind has released Long-Context Frontiers (LOFT), a benchmark for LLMs that can process hundreds of thousands or millions of tokens in one prompt.

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Energy-Based World Models bring human-like cognition to AI https://bdtechtalks.com/2024/06/24/energy-based-world-models/?utm_source=rss&utm_medium=rss&utm_campaign=energy-based-world-models https://bdtechtalks.com/2024/06/24/energy-based-world-models/#respond Mon, 24 Jun 2024 12:12:15 +0000 https://bdtechtalks.com/?p=21738 Energy-based world models (EBWM) enable AI systems to reflect on their predictions and achieve human-like cognitive abilities missing in autoregressive models.

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HippoRAG takes cues from the brain to improve LLM retrieval https://bdtechtalks.com/2024/06/17/hipporag-llm-retrieval/?utm_source=rss&utm_medium=rss&utm_campaign=hipporag-llm-retrieval https://bdtechtalks.com/2024/06/17/hipporag-llm-retrieval/#respond Mon, 17 Jun 2024 06:41:00 +0000 https://bdtechtalks.com/?p=21694 HippoRAG is a technique inspired from the interactions between the cortex and hippocampus to improve knowledge retrieval for large language models (LLM).

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How to boost language models with graph neural networks https://bdtechtalks.com/2024/06/10/gnn-rag/?utm_source=rss&utm_medium=rss&utm_campaign=gnn-rag https://bdtechtalks.com/2024/06/10/gnn-rag/#respond Mon, 10 Jun 2024 08:39:04 +0000 https://bdtechtalks.com/?p=21640 GNN-RAG brings together the knowledge graph–processing abilities of graph neural networks and the language abilities of LLMs to unlock new applications.

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DeepSeek-Prover uses synthetic data to boost theorem proving in LLMs https://bdtechtalks.com/2024/06/03/deepseek-prover/?utm_source=rss&utm_medium=rss&utm_campaign=deepseek-prover https://bdtechtalks.com/2024/06/03/deepseek-prover/#respond Mon, 03 Jun 2024 06:32:55 +0000 https://bdtechtalks.com/?p=21606 DeepSeek has created an algorithm that enables an LLM to bootstrap itself by starting with a small dataset of labeled theorem proofs and create increasingly higher quality example to fine-tune itself.

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How to optimize ChatGPT and other LLMs for software engineering https://bdtechtalks.com/2024/05/27/chatgpt-software-engineering/?utm_source=rss&utm_medium=rss&utm_campaign=chatgpt-software-engineering https://bdtechtalks.com/2024/05/27/chatgpt-software-engineering/#respond Mon, 27 May 2024 06:33:30 +0000 https://bdtechtalks.com/?p=21571 A new study shows the strengths and pain points of using ChatGPT in software engineering. The can help organizations turbocharge their developers with LLMs.

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Boost LLM application development with many-shot learning https://bdtechtalks.com/2024/05/20/long-context-llm-applications/?utm_source=rss&utm_medium=rss&utm_campaign=long-context-llm-applications https://bdtechtalks.com/2024/05/20/long-context-llm-applications/#respond Mon, 20 May 2024 13:00:00 +0000 https://bdtechtalks.com/?p=21523 A study by Carnegie Mellon University and Tel Aviv University shows that many-shot learning with long-context LLMs matches retrieval (RAG) and fine-tuning.

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How far can you trust chain-of-thought prompting? https://bdtechtalks.com/2024/05/13/chain-of-thought-planning/?utm_source=rss&utm_medium=rss&utm_campaign=chain-of-thought-planning https://bdtechtalks.com/2024/05/13/chain-of-thought-planning/#respond Mon, 13 May 2024 13:04:21 +0000 https://bdtechtalks.com/?p=21457 A new study shows that chain-of-thought (CoT) prompts only improve large language models (LLM) on very narrow planning tasks and don't generalize broadly.

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Train your LLMs to choose between RAG and internal memory automatically https://bdtechtalks.com/2024/05/06/adapt-llm/?utm_source=rss&utm_medium=rss&utm_campaign=adapt-llm https://bdtechtalks.com/2024/05/06/adapt-llm/#comments Mon, 06 May 2024 13:00:00 +0000 https://bdtechtalks.com/?p=21419 Adapt-LLM is a technique that enables language models to choose between their parametric memory and getting help from an information retrieval (RAG) system.

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What OpenELM language models say about Apple’s generative AI strategy https://bdtechtalks.com/2024/04/29/apple-openelm/?utm_source=rss&utm_medium=rss&utm_campaign=apple-openelm https://bdtechtalks.com/2024/04/29/apple-openelm/#respond Mon, 29 Apr 2024 13:00:00 +0000 https://bdtechtalks.com/?p=21384 Apple has released the full code, weights, checkpoints, and more for OpenELM, its latest language models. Here is what it means for its generative AI strategy.

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How to turn any LLM into an embedding model https://bdtechtalks.com/2024/04/22/llm2vec/?utm_source=rss&utm_medium=rss&utm_campaign=llm2vec https://bdtechtalks.com/2024/04/22/llm2vec/#comments Mon, 22 Apr 2024 13:00:00 +0000 https://bdtechtalks.com/?p=21341 Researchers at Quebec AI Institute (Mila) have released LLM2Vec, a technique that can turn any decoder-only LLM into a universal embedding model.

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Stanford’s ReFT fine-tunes LLMs at a fraction of the cost https://bdtechtalks.com/2024/04/15/reft-llm-fine-tuning/?utm_source=rss&utm_medium=rss&utm_campaign=reft-llm-fine-tuning https://bdtechtalks.com/2024/04/15/reft-llm-fine-tuning/#respond Mon, 15 Apr 2024 13:00:00 +0000 https://bdtechtalks.com/?p=21292 Representation Fine-Tuning (ReFT) is a technique to fine-tune LLMs for specific tasks based by only modifying a small fraction of their representations.

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Compress GPT-4 and Claude prompts with LLMLingua-2 https://bdtechtalks.com/2024/04/01/llmlingua-2-prompt-compression/?utm_source=rss&utm_medium=rss&utm_campaign=llmlingua-2-prompt-compression https://bdtechtalks.com/2024/04/01/llmlingua-2-prompt-compression/#respond Mon, 01 Apr 2024 13:12:38 +0000 https://bdtechtalks.com/?p=21187 LLMLingua-2 is a prompt compression technique by Microsoft that can reduce the size of prompts by up to five times.

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How to fine-tune LLMs for better RAG performance https://bdtechtalks.com/2024/03/25/raft-llm-fine-tuning-for-rag/?utm_source=rss&utm_medium=rss&utm_campaign=raft-llm-fine-tuning-for-rag https://bdtechtalks.com/2024/03/25/raft-llm-fine-tuning-for-rag/#respond Mon, 25 Mar 2024 14:00:00 +0000 https://bdtechtalks.com/?p=21127 Retrieval Augmented Fine Tuning (RAFT) combines supervised fine-tuning with RAG to improve LLM domain knoweldge and ability to use in-context documents.

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Netflix study shows limits of cosine similarity in embedding models https://bdtechtalks.com/2024/03/21/netflix-cosine-similarity-embedding-models/?utm_source=rss&utm_medium=rss&utm_campaign=netflix-cosine-similarity-embedding-models https://bdtechtalks.com/2024/03/21/netflix-cosine-similarity-embedding-models/#comments Thu, 21 Mar 2024 14:28:51 +0000 https://bdtechtalks.com/?p=21111 Blindly using cosine similarity in embedding models can have arbitrary and therefore meaningless similarities, a research by Netflix shows.

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How to customize LLMs for low-frequency topics https://bdtechtalks.com/2024/03/18/llm-rag-vs-fine-tuning/?utm_source=rss&utm_medium=rss&utm_campaign=llm-rag-vs-fine-tuning https://bdtechtalks.com/2024/03/18/llm-rag-vs-fine-tuning/#respond Mon, 18 Mar 2024 14:00:00 +0000 https://bdtechtalks.com/?p=21085 New study provides insights on the effectiveness of LLM RAG and fine-tuning for topics that are not included in the model's training data.

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How to improve the throughput of LLM application servers https://bdtechtalks.com/2024/03/12/llm-relay-attention/?utm_source=rss&utm_medium=rss&utm_campaign=llm-relay-attention https://bdtechtalks.com/2024/03/12/llm-relay-attention/#respond Tue, 12 Mar 2024 14:00:00 +0000 https://bdtechtalks.com/?p=21049 RelayAttention is a technique that increases the throughput of LLM servers by reducing memory access to KV values of system prompts.

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Diffusion models are now turbocharging reinforcement learning systems https://bdtechtalks.com/2024/03/04/diffusion-world-model/?utm_source=rss&utm_medium=rss&utm_campaign=diffusion-world-model https://bdtechtalks.com/2024/03/04/diffusion-world-model/#respond Mon, 04 Mar 2024 14:00:00 +0000 https://bdtechtalks.com/?p=20996 Diffusion models are best known for their image-generation abilities. Now, they are being used to learn world models for reinforcement learning systems.

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How language models can teach themselves to follow instructions https://bdtechtalks.com/2024/01/29/self-rewarding-language-models/?utm_source=rss&utm_medium=rss&utm_campaign=self-rewarding-language-models https://bdtechtalks.com/2024/01/29/self-rewarding-language-models/#respond Mon, 29 Jan 2024 14:00:00 +0000 https://bdtechtalks.com/?p=19577 Meta and NYU have released "self-rewarding language models" a technique that enables LLMs to self-improve for instruction-following.

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