Yue Xing

Yingqian Cui, Pengfei He, Jingying Zeng et al.

Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks. However, the detailed reasoning process in CoT often incurs long generation times and high computational costs, partly due to the inclusion of unnecessary steps. To address this, we propose a method to identify critical reasoning steps using perplexity as a measure of their importance: a step is deemed critical if its removal causes a significant increase in perplexity. Our method enables models to focus solely on generating these critical steps. This can be achieved through two approaches: refining demonstration examples in few-shot CoT or fine-tuning the model using selected examples that include only critical steps. Comprehensive experiments validate the effectiveness of our method, which achieves a better balance between the reasoning accuracy and efficiency of CoT.

Shenglai Zeng, Jiankun Zhang, Bingheng Li et al.

Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, often leading to issues with misleading or unhelpful information. This work aims to provide a systematic study on knowledge checking in RAG systems. We conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. Motivated by the findings, we further develop representation-based classifiers for knowledge filtering. We show substantial improvements in RAG performance, even when dealing with noisy knowledge databases. Our study provides new insights into leveraging LLM representations for enhancing the reliability and effectiveness of RAG systems.

Meiqi Liu, Zhuoqun Huang, Yue Xing

With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream tasks. However, due to the high computation complexity of adversarial training, it is not feasible to fine-tune the foundation model to improve its robustness on the downstream task. Observing the above challenge, we want to improve the downstream robustness without updating/accessing the weights in the foundation model. Inspired from existing literature in robustness inheritance (Kim et al., 2020), through theoretical investigation, we identify a close relationship between robust contrastive learning with the adversarial robustness of supervised learning. To further validate and utilize this theoretical insight, we design a simple-yet-effective robust auto-encoder as a data pre-processing method before feeding the data into the foundation model. The proposed approach has zero access to the foundation model when training the robust auto-encoder. Extensive experiments demonstrate the effectiveness of the proposed method in improving the robustness of downstream tasks, verifying the connection between the feature robustness (implied by small adversarial contrastive loss) and the robustness of the downstream task.

Shenglai Zeng, Pengfei He, Kai Guo et al.

Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend to over-rely on external knowledge, making them vulnerable to misleading and unhelpful contexts. To address this, we propose the concept of context-robust LLMs, which can effectively balance internal knowledge with external context, similar to human cognitive processes. Specifically, context-robust LLMs should rely on external context only when lacking internal knowledge, identify contradictions between internal and external knowledge, and disregard unhelpful contexts. To achieve this goal, we introduce Grft, a lightweight and plug-and-play gated representation fine-tuning approach. Grft consists of two key components: a gating mechanism to detect and filter problematic inputs, and low-rank representation adapters to adjust hidden representations. By training a lightweight intervention function with only 0.0004\% of model size on fewer than 200 examples, Grft can effectively adapt LLMs towards context-robust behaviors.