5 accepted papers
While chain-of-thought (CoT) distillation from advanced large language models (LLMs) has proven effective in general reasoning tasks, it struggles in scientific domains where even advanced models often produce incorrect or superficial reasoning due to high complexity and specialized knowledge requir…
Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis. While large language models (LLMs) have shown remarkable proficiency in analyzing and reasoning through complex tasks, they still encounte…
Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and *denovo* design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibil…
Evaluating the response quality of large language models (LLMs) for open-ended questions poses a significant challenge, especially given the subjectivity and multi-dimensionality of "quality" in natural language generation. Existing LLM evaluators often neglect that different scenarios require disti…
The past years have witnessed a proliferation of large language models (LLMs). Yet, reliable evaluation of LLMs is challenging due to the inaccuracy of standard metrics in human perception of text quality and the inefficiency in sampling informative test examples for human evaluation. This paper pre…