4 citations · 16 across the 48 of their papers we have counts for
5 papers · 2 filters
ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
Wangtao Sun, Xuanqing Yu, Shizhu He +2
Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers. However, LLMs still fail in many specific tasks a…
TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
Fangyu Lei, Tongxu Luo, Pengqi Yang +8
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the…
Generative Calibration for In-context Learning
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu +2
As one of the most exciting features of large language models (LLMs), in-context learning is a mixed blessing. While it allows users to fast-prototype a task solver with only a few…
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
Yifan Wei, Yisong Su, Huanhuan Ma +5
Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs hav…
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models
Fangyu Lei, Qian Liu, Yiming Huang +3
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning. However, as LLMs are able to pro…