Survey on Hallucination in LLM; LLM’s Understanding Math; GPT4All Open-Source LMs; Next Chapter of Gemini; Improved GPT-4 Performance; and More.
Danny Butvinik
Chief Data Scientist | 100K+ Followers | FinCrime | Writer | Author of AI Vanguard Newsletter
Editor's Paper Recommendations
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4: In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for various scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge-integration tasks. We evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions: The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a critical tendency to produce hallucinations, resulting in content that is inconsistent with real-world facts or user inputs. This phenomenon poses substantial challenges to their practical deployment. It raises concerns over the reliability of LLMs in real-world scenarios, which attracts increasing attention to detecting and mitigating these hallucinations. This survey aims to provide a thorough overview of recent advances in LLM hallucinations. We begin with an innovative taxonomy of LLM hallucinations, then delve into the factors contributing to hallucinations. After that, we will present a comprehensive overview of hallucination detection methods and benchmarks. Additionally, representative approaches designed to mitigate hallucinations are introduced accordingly. Finally, we analyze the challenges that highlight the current limitations and formulate open questions to delineate pathways for future research on hallucinations in LLMs.
GPT4All: An Ecosystem of Open Source Compressed Language Models: Large language models (LLMs) have recently achieved human-level performance on various professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure, are only accessible via rate-limited, geo-locked, and censored web interfaces, and lack publicly available code and technical reports. This paper tells the story of GPT4All, a popular open-source repository that aims to democratize LLM access. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully-fledged open-source ecosystem. We hope this paper will provide you with a technical overview of the original GPT4All models and a case study on the subsequent growth of the GPT4All open-source ecosystem.
Large Language Models' Understanding of Math: Source Criticism and Extrapolation: It has been suggested that large language models such as GPT-4 have acquired some form of understanding beyond the correlations among the words in the text, including some understanding of mathematics. Here, we perform a critical inquiry into this claim by evaluating the mathematical understanding of the GPT-4 model. Considering that GPT-4's training set is a secret, it is not straightforward to evaluate whether the model's correct answers are based on a mathematical understanding or a replication of proofs that the model has seen before. We specifically craft mathematical questions whose formal proofs are not readily available on the web, proofs that are more likely not seen by the GPT-4. We see that GPT-4 cannot solve those problems despite their simplicity. It is hard to find scientific evidence suggesting that GPT-4 has acquired an understanding of even basic mathematical concepts. A straightforward way to find failure modes of GPT-4 in theorem proving is to craft questions where their formal proofs are not available on the web. Our finding suggests that GPT-4 can reproduce, rephrase, and polish the mathematical proofs that it has seen before and not in grasping mathematical concepts. We also see that GPT-4's ability to prove mathematical theorems continuously expands over time despite the claim that it is a fixed model. Proving mathematical theorems in a formal language is comparable to the methods used in search engines such as Google, while predicting the next word in a sentence may be a misguided approach. This recipe often leads to excessive extrapolation and eventual failures. Prompting the GPT-4 over and over may benefit the GPT-4 and the OpenAI, but we question whether it is valuable for machine learning or theorem proving.
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