Instruction-following models are attractive alternatives to fine-tuned approaches for question answering (QA). By simply prepending relevant documents and an instruction to their input, these models can be adapted to various information domains and tasks without additional training. However, these models tend to produce verbose responses with supplementary information, which makes traditional QA metrics like exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we evaluate instruction-following models along two fronts: 1) how well they satisfy user’s information need (correctness), and 2) whether they disseminate information supported by the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness and propose simple token-overlap metrics that correlate highly with human judgments. Our analysis reveals that for correctness, instruction-following models perform comparably to models specifically fine-tuned for that task. However, they struggle to accurately judge the relevance of the provided knowledge and often hallucinate in their responses. We hope our work encourages more holistic evaluation of instruction-following models for QA. Our code and human annotation data is available at https://github.com/McGill-NLP/instruct-qa.

Augmenting pretrained language models with retrievers has shown promise in effectively solving common NLP problems, such as language modeling and question answering. In this paper, we evaluate the strengths and weaknesses of popular retriever-augmented language models, namely kNN-LM, REALM, DPR + FiD, Contriever + ATLAS, and Contriever + Flan-T5, in reasoning over retrieved statements across different tasks. Our findings indicate that the simple similarity metric employed by retrievers is insufficient for retrieving all the necessary statements for reasoning. Additionally, the language models do not exhibit strong reasoning even when provided with only the required statements. Furthermore, when combined with imperfect retrievers, the performance of the language models becomes even worse, e.g., Flan-T5’s performance drops by 28.6% when retrieving 5 statements using Contriever. While larger language models improve performance, there is still a substantial room for enhancement. Our further analysis indicates that multihop retrieve-and-read is promising for large language models like GPT-3.5, but does not generalize to other language models like Flan-T5-xxl. The code is available at https://github.com/McGill-NLP/retriever-lm-reasoning.

Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in Natural Language Processing (NLP) tasks. However, there are still some shortcomings in the Masked Language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. Then, we propose Multi-Graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taking advantage of a static multi-graph containing global word co-occurrences in the text corpus beside global real-world facts about words in knowledge graphs. The proposed model also employs a dynamic sentence graph to capture local context effectively. Experimental results demonstrate that our model can considerably enhance the performance in the MLM task.