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Monte Carlo Sampling for Analyzing In-Context Examples
Stephanie Schoch, Yangfeng Ji
Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on s…
In-Context Learning (and Unlearning) of Length Biases
Stephanie Schoch, Yangfeng Ji
Large language models have demonstrated strong capabilities to learn in-context, where exemplar input-output pairings are appended to the prompt for demonstration. However, existin…
A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension
Saahith Janapati, Yangfeng Ji
The performance of Large Language Models (LLMs) on natural language tasks can be improved through both supervised fine-tuning (SFT) and in-context learning (ICL), which operate via…
Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation
Wanyu Du, Yangfeng Ji
The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge te…