Zobrazeno 1 - 10
of 282
pro vyhledávání: '"Wallace, Byron C"'
Autor:
Arroyo, Alberto Mario Ceballos, Munnangi, Monica, Sun, Jiuding, Zhang, Karen Y. C., McInerney, Denis Jered, Wallace, Byron C., Amir, Silvio
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicia
Externí odkaz:
http://arxiv.org/abs/2407.09429
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repetition in models, beyond frequent n-grams. Specifically, we define synta
Externí odkaz:
http://arxiv.org/abs/2407.00211
Eliciting "chain of thought" (CoT) rationales -- sequences of token that convey a "reasoning" process -- has been shown to consistently improve LLM performance on tasks like question answering. More recent efforts have shown that such rationales can
Externí odkaz:
http://arxiv.org/abs/2406.14511
Entity matching is the task of linking records from different sources that refer to the same real-world entity. Past work has primarily treated entity linking as a standard supervised learning problem. However, supervised entity matching models often
Externí odkaz:
http://arxiv.org/abs/2406.09330
Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treatment effectiveness, results from meta-analyses are considered the stro
Externí odkaz:
http://arxiv.org/abs/2405.01686
Autor:
Munnangi, Monica, Feldman, Sergey, Wallace, Byron C, Amir, Silvio, Hope, Tom, Naik, Aakanksha
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In this work we set out to improve LLM performance on biomedical NER in lim
Externí odkaz:
http://arxiv.org/abs/2404.00152
The diversity across outputs generated by large language models shapes the perception of their quality and utility. Prompt leaks, templated answer structure, and canned responses across different interactions are readily noticed by people, but there
Externí odkaz:
http://arxiv.org/abs/2403.00553
Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now wish to choose confidently, but with minimal effort, the best perform
Externí odkaz:
http://arxiv.org/abs/2402.18756
With the advent of large language models (LLMs), there has been growing interest in exploring their potential for medical applications. This research aims to investigate the ability of LLMs, specifically ChatGPT, in the context of pharmacovigilance e
Externí odkaz:
http://arxiv.org/abs/2402.15663
Autor:
Krishna, Kundan, Ramprasad, Sanjana, Gupta, Prakhar, Wallace, Byron C., Lipton, Zachary C., Bigham, Jeffrey P.
LLMs can generate factually incorrect statements even when provided access to reference documents. Such errors can be dangerous in high-stakes applications (e.g., document-grounded QA for healthcare or finance). We present GenAudit -- a tool intended
Externí odkaz:
http://arxiv.org/abs/2402.12566