Why Chain of Thought Fails in Clinical Text Understanding

QSTN has been introduced as an open-source Python framework designed to generate responses from questionnaire-style prompts, facilitating in-silico surveys and annotation tasks with large language models (LLMs). The framework allows for robust evaluation of questionnaire presentation and response generation methods, based on an extensive analysis of over 40 million survey responses.

The evaluation of large language models (LLMs) is increasingly reliant on classifiers, either LLMs or human annotators, to assess desirable or undesirable behaviors. A recent study highlights that traditional metrics like Accuracy and F1 can be misleading due to class imbalances, advocating for the use of Youden's J statistic and Balanced Accuracy as more reliable alternatives for selecting evaluators.

Large language models (LLMs) are increasingly utilized for factual inquiries, yet their internal representations of truth remain inadequately understood. A recent study introduces the concept of representational stability, assessing how robustly LLMs differentiate between true, false, and ambiguous statements through controlled experiments involving linear probes and model activations.

A recent empirical study on Large Language Models (LLMs) has revealed that the effectiveness of many-shot prompting for code translation may be overstated. Analyzing over 90,000 translations, researchers found that while more examples can improve static similarity metrics, functional correctness peaks with fewer examples, indicating a 'many-shot paradox'.

Large language models (LLMs) exhibit two mechanisms of value expression: intrinsic, based on learned values, and prompted, based on explicit prompts. This study analyzes these mechanisms at a mechanistic level, revealing both shared and unique components in their operation.

A recent study has introduced a systematic evaluation framework for aligning large language models (LLMs) with diverse human preferences in federated learning environments. This framework assesses the trade-off between alignment quality and fairness using various aggregation strategies for human preferences, including a novel adaptive scheme that adjusts preference weights based on historical performance.

A comprehensive study has been conducted on the use of large language models (LLMs) for synthesizing public deliberations into neutral summaries. The research highlights the potential of LLMs to generate summaries while also addressing concerns regarding their ability to represent minority perspectives and biases related to input order. The study introduces DeliberationBank, a dataset created from contributions by 3,000 participants, aimed at evaluating LLM performance in summarization tasks.

Large language models (LLMs) are increasingly being integrated into multi-agent systems (MAS), where peer interactions significantly influence decision-making. A recent study introduces KAIROS, a benchmark designed to simulate collaborative quiz-style interactions among peer agents, allowing for a detailed analysis of how rapport and peer behaviors affect LLMs' decision-making processes.