Vincent Granville

🔔 NEW RESEARCH BLOG: Since we are at ICLR this week, we're publishing a series of research blogs focused on our accepted papers to give you a deeper look at this exciting research. First up, "ConR: Contrastive Regularizer for Deep Imbalanced Regression" by M. Keramati, L. Meng, R. David Evans - A contrastive regularizer models global and local label similarities in feature space and prevents the features of minority samples from being collapsed into their majority neighbours. You can read the blog here ➡️ https://bit.ly/4dsTKeb. #ICLR #ImbalancedRegression #ICLR2024 #ConR #ICLRpapers #AIresearch #ML #ICLRpapers #ICLRinVienna #ContrastiveRegularizer

13

Understanding Dense and Sparse Retrieval: 🔹 Methods 🔹 Models 🔹 Semantic Capabilities 👉 Follow: https://lnkd.in/gACKRw2J #llm #ai #AI #LLM #GenAI #RAG #DataScience

2

Separating Fact from Logic: Test of Time ToT Benchmark Isolates Reasoning Skills in LLMs for Improved Temporal Understanding Quick read: https://lnkd.in/gbc5CqN8 Paper: https://lnkd.in/giu72CwW HF Page: https://lnkd.in/g7H6dgY9

17

In computational linguistics, researchers are focused on enhancing language models' ability to extract precise information from extensive textual data. Recent studies introduce "retrieval heads," specialized attention mechanisms that selectively focus on critical segments of large texts, improving targeted data retrieval. These heads, integrated into transformer-based models like LLaMA and Yi, significantly enhance accuracy and efficiency, particularly in handling long-context scenarios. The methodology involves thorough experiments across various models, including the Needle-in-a-Haystack test, which embeds specific information to measure precision. Results show models with active retrieval heads outperform those without, with accuracy dropping notably when these heads are masked. The empirical evidence underscores the effectiveness of retrieval heads in improving information retrieval precision and reliability. This research deepens our understanding of attention mechanisms in large-scale text processing, paving the way for more efficient and accurate language models. https://lnkd.in/dNNPvMXw https://lnkd.in/ddbHX6EA Innovation Hacks AI #aiinnovation #generativeai

1

Hallucinations in LLMs....problem solved? Not yet but getting there. Scientists have developed a promising method for detecting hallucinations using a multi-layered LLM approach to identify instances where AI systems confidently generate inaccurate or fabricated information.  This is a 4-eye approach to checking the response to a prompt implemented using additional LLMs. By focusing on semantic meaning and paraphrase detection, the system shows potential for improving the reliability and trustworthiness of the initial LLM. However it could also potentially amplify the bias and hallucination, instead of 'yes, but' the system could respond with 'yes, and'. Learn more about this development in the latest issue of Nature. #AI #LLM #Innovation #TechAdvancement https://lnkd.in/gWudvaV6

7

1 Comment

Recent studies show transformer decoders in models like GPT, LLaMA, OPT, and BLOOM exhibit a near-perfect linear relationship across layers 🤔 👀 Experiments show that removing or linearly approximating some of the most linear blocks of transformers does not affect significantly the loss or model performance. 💁 This study challenges the existing understanding of transformer architectures, suggesting that their operation may be more linear than previously assumed. —————————————————— Want to stay at the forefront of Generative AI developments? Follow NLPlanet for daily insights into the most relevant news, guides, and research! 🚀

11

A Recent Stanford Study Evaluates the Evolution of Multimodal Foundation Models from Few-Shot to Many-Shot-In-Context Learning In the dynamic landscape of large language models (LLMs) and large multimodal models (LMMs), the integration of demonstrating examples, known as in-context learning (ICL), has emerged as a pivotal enhancement strategy. Recent research from Stanford University sheds light on the transformative impact of incorporating in-context learning, particularly in the realm of multimodal models scaling from few-shot to many-shot scenarios. This investigation, led by a team of researchers, delves into the efficacy of advanced models such as GPT-4o and Gemini 1.5 Pro, shedding light on their performance dynamics in response to increasing demonstrating examples. https://is.gd/d6Jk9m #AI #AItechnology #artificialintelligence #incontextlearning #llm #LMMs #machinelearning

1

🚀 Modern Large Language Models (LLMs) are meticulously fine-tuned for instruction-following and safety. They're trained to refuse harmful requests, like answering "How can I make a bomb?" with a firm "Sorry, I cannot help you." 🔍 Recent findings reveal a single direction in the residual stream that mediates this refusal. Blocking this direction stops the model from refusing harmful requests, while adding it makes the model refuse even harmless ones. 🧠 This phenomenon is consistent across different open-source models and scales, highlighting a simple modification to model weights that can jailbreak these models without fine-tuning or extra interventions. While this isn't new—safety guardrails have been known to be bypassed easily—this technique underlines the fragility of safety fine-tuning in open-source chat models. 👉 For an impactful AI journey, drop us a message at ai@electiconsulting.com. #llm #largelanguagemodels #ai #artificialintelligence #jailbreak https://lnkd.in/dvaUUAN6

17

Fine-tuning LLMs with Reinforcement Learning from Human Feedback This blog post explores how Reinforcement Learning from Human Feedback (RLHF) can be used to fine-tune Large Language Models (LLMs) to align with human preferences. It delves into the concept of Proximal Policy Optimization (PPO) and how it is used in this context. The key takeaway is that RLHF offers a powerful framework to train LLMs that can produce human-aligned text, while still maintaining creativity. This paves the way for more natural and nuanced language generation in the future of AI and natural language understanding. Read the article: https://lnkd.in/d9EGf579 #AI #machinelearning #goML

8

This paper proposed a #Machine #Learning (#ML)-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, their results show that #Light #Gradient #Boosting #Machine (#LightGBM) algorithm overall outperforms others, even with sparse training data, by providing a 65% increase in prediction accuracy as compared to empirical models and 13x decrease in prediction time as compared to ray-tracing. ---- Usama Masood, Hasan Farooq, Ali Imran, Adnan Abu-Dayya More details can be found at this link: https://lnkd.in/e7FtFCi4

5

🙌🏼 Our latest issue (6.2) has published! For an excellent preview of every article, start the issue 📖 by first reading Editor-in-Chief Xiao-Li MENG's editorial, Data Science Experiences & Emotions: From Lonesome to Awesome. 🧐 Check it out now! #datascience Harvard Data Science Initiative The MIT Press

1

Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the latest progress. This article summarizes some of the most important LLM papers published during the Fourth Week of May 2024. The papers cover various topics shaping the next generation of language models, from model optimization and scaling to reasoning, benchmarking, and enhancing performance. Keeping up with novel LLM research across these domains will help guide continued progress toward models that are more capable, robust, and aligned with human values. ⭐ Subscribe to the newsletter To Data & Beyond to have full access to my past and future articles: https://lnkd.in/dVkD-7WT 📒 My eBook Data Science Portfolio for Success: Your Ultimate Guide to Building a Data Science Portfolio: https://lnkd.in/ddaqcmt8

5

🚀 Research Paper Highlights: Let's find out impact of full fine tuning on LLMs! Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? by Zorik Gekhman et al from Israel Institute of Technology and Google Research. 1. Acquiring New Knowledge and Hallucinations: This study investigates how fine-tuning LLMs with new factual knowledge affects their tendency to generate hallucinations relative to their pre-existing knowledge. When LLMs undergo supervised fine-tuning, they encounter new information not seen during pre-training, potentially leading to the generation of factually incorrect responses. 🚫 2. Challenges and Risks: The research highlights that LLMs struggle to acquire new knowledge through fine-tuning, learning it significantly slower than pre-existing information. Additionally, this process increases the model’s propensity for hallucinations, underscoring the risks of introducing new factual knowledge ⚠️ and the critical role of pre-training for knowledge acquisition. 3. Knowledge Assessment with SliCK: To evaluate the impact of new knowledge, the SliCK framework categorizes information into Known and Unknown types. Known examples are further divided into HighlyKnown, MaybeKnown, and WeaklyKnown. This hierarchical method facilitates a controlled study on closed-book question answering (QA), assessing the model's ability to integrate new information. 4. Practical Mitigation Strategies: Early-stopping can significantly reduce the risk of hallucinations, which typically emerge during later training stages. Alternatively, filtering out Unknown fine-tuning examples effectively minimizes overfitting risk without compromising performance. ✅ In conclusion, LLMs struggle to integrate new factual knowledge through fine-tuning, suggesting that fine-tuning is less effective for introducing new information. This emphasizes the importance of robust pre-training for factual knowledge acquisition. 💡 Further reading- https://lnkd.in/e4FZQ7SR 🌟 Stay tuned for more updates on upcoming research and analysis in this rapidly evolving landscape of Generative AI. #LLMs #finetuning #hallucinations #innovation #research

8

Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the latest progress. This article summarizes some of the most important LLM papers published during the First Week of June 2024. The papers cover various topics shaping the next generation of language models, from model optimization and scaling to reasoning, benchmarking, and enhancing performance. Keeping up with novel LLM research across these domains will help guide continued progress toward models that are more capable, robust, and aligned with human values. ➡ Do not miss the 60% discount on To Data & Beyond yearly subscription: https://lnkd.in/gmwFUqYr

2