Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification (2024)

Abstract: In recent years, there has been a growing interest in utilizing external knowledge to reduce hallucinations in large language models (LLMs) and provide them with updated information. Despite this improvement, a major challenge lies in the lack of explicit citations, which hampers the ability to verify the information generated by these models.This paper focuses on providing models with citation capabilities efficiently. By constructing a dataset of citations, we train two model architectures: an FID-style FLAN-T5 model for efficient answer composition and a 13B model known for its success in instruction following after tuning. Evaluation on fluency, correctness, and citation quality is conducted through human assessment and the newly introduced Automatic LLMs’ Citation Evaluation (ALCE) benchmark.Results demonstrate significant improvements in answer quality and efficiency, surpassing the performance of the popular ChatGPT on some of the metrics. The models exhibit exceptional out-of-domain generalization in both human and automatic evaluation. Notably, the FID-style FLAN-T5 model with only 3B parameters performs impressively compared to the 13B model.

## related work webgpt @xcite , is trained on humans demonstrations on how to use the web environment by issuing commands and answering questions. these demonstrations are used to finetune a pretrained gpt-3 @xcite model. subsequently, a reward model is employed for reinforcement learning with human feedback (rlhf), which further improves the model's performance. in contrast, gophercite @xcite focuses on reading comprehension by utilizing a large context that includes thousands of tokens from multiple pages. gophercite also utilizes rlhf to fine-tune the model based on human preferences. both approaches highlight the significance of larger model sizes, with webgpt using a 175 billion parameter model and gophercite employing a 280 billion parameter model. attributed question answering @xcite involves answering a question while simultaneously providing a reference to a brief segment of text that supports the answer. unlike our proposed methods where the link to the reference accompanies the answer directly, in this approach, the model generates the answer along with the reference link as an attribute. ## conclusion in conclusion, this work improves the reliability and verifiability of question answering by addressing the challenge of explicit citations efficiently. this is achieved by constructing a citation training data and fine-tuning two models: an fid-flan-t5 model optimized for answer composition and a 13b llama model. the evaluation, conducted through human assessment and the alce benchmark, demonstrates notable improvement in answer quality, and citation accuracy while being far more efficient than the baselines. the presented models surpass the popular chatgpt and exhibit remark-able out-of-domain generalization, as evidenced by both human and automatic evaluation. particularly, the 3b fid-style flan-t5 model, despite its smaller size, performs exceptionally well and competes with our 13b llama model. future research could explore tuning models capable of both question answering and instruction following.