Hurdles to Progress in Long-form Question Answering (2103.06332v2)

Abstract: The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. To demonstrate these challenges, we first design a new system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. While our system tops the public leaderboard, a detailed analysis reveals several troubling trends: (1) our system's generated answers are not actually grounded in the documents that it retrieves; (2) ELI5 contains significant train / validation overlap, as at least 81% of ELI5 validation questions occur in paraphrased form in the training set; (3) ROUGE-L is not an informative metric of generated answer quality and can be easily gamed; and (4) human evaluations used for other text generation tasks are unreliable for LFQA. We offer suggestions to mitigate each of these issues, which we hope will lead to more rigorous LFQA research and meaningful progress in the future.

Insights on "Hurdles to Progress in Long-form Question Answering"

Kalpesh Krishna, Aurko Roy, and Mohit Iyyer's paper, "Hurdles to Progress in Long-form Question Answering," explores the complexities and intrinsic challenges present within the long-form question answering (LFQA) task. This investigation reveals several critical roadblocks that deter substantive advancements in efficiently modeling LFQA systems, primarily focusing on the KILT benchmark and the ELI5 dataset.

The paper presents a state-of-the-art LFQA model using sparse attention and contrastive retriever learning, which tops the leaderboard on the ELI5 dataset. However, success in these metrics is questioned through a rigorous analysis, revealing substantial issues in both evaluation metrics and dataset construction that currently prevent meaningful progress. The paper identifies four main hurdles: ungrounded answer generation, train-validation overlap, ineffective performance metrics, and unreliable human evaluations.

Key Findings and Claims

1. Ungrounded Answer Generations: The authors demonstrate that models generate answers not based on retrieved documents, which are ideally supposed to inform the answers. By introducing a method where models are conditioned on random document retrievals, they find negligibly different outcomes in generated answer quality. This suggests that retrieval models do not significantly impact the generation process, questioning the foundation of retrieval-augmented generation systems.
2. Train-Validation Overlap: Through innovative annotation techniques, the paper uncovers overwhelming overlap in the ELI5 dataset, where about 81% of validation questions have paraphrases in the training set. This overlap suggests that models might leverage memorization from training rather than demonstrating genuine generalization, fundamentally questioning the validity of performance improvements reported on such datasets.
3. Inadequacy of ROUGE-L Metrics: The emphasis on ROUGE-L scores to evaluate LFQA models is criticized as being misaligned with human judgment. The paper reveals that non-answering strategies like repeating the question or utilizing a random training set answer can outperform genuine attempts in terms of ROUGE-L, highlighting how the metric fails to capture answer relevance or factual accuracy.
4. Challenges in Human Evaluation: Human evaluations, crucial for gauging the effectiveness of generated answers in LFQA, also appear problematic. Assessors struggle with topics they are unfamiliar with, leading to unreliable assessments of answer correctness, and long answer lengths make human evaluation impractically challenging, highlighting a need for more streamlined human assessment approaches.

Implications and Future Directions

The ramifications of these findings suggest that the current state of LFQA research, while advancing computational metrics, lacks in fostering true performance improvements in real-world applicability. The authors recommend new strategies for dataset curation, emphasizing the elimination of paraphrase overlaps and suggesting the use of domain-specific holdouts for evaluation to push for genuine generalization capabilities.

Moreover, the paper calls for the development of new evaluation metrics beyond ROUGE-L that are capable of assessing the coherence and factual correctness of long-form answers. Innovative systems should aim for models grounded in retrieval, potentially utilizing enhanced generative architectures that facilitate grounding and evaluation metrics that genuinely reflect the utility of the LFQA system in practical applications.

In summary, the work by Krishna et al. illuminates the substantial gaps and obstacles in the pathway toward reliable LFQA solutions. Forward-looking research will need to address these facets, focusing on improved dataset construction, robust task-specific metrics, and human evaluation methodologies to foster a leap in LFQA capabilities toward more accurate and contextually grounded text generation.