Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP

We introduce Efficient Title Reranker via Broadcasting Query Encoder, a novel title reranking technique to achieve efficient title reranking 20x-40x faster than vanilla passage reranker. However, one of the challenges with the training of Efficient Title Reranker is the instability. Analyzing the issue, we found some very difficult ground truths might act as noisy labels causing accuracy to drop as well as some extreme values in model probability output causing nan. To address these issues, we introduce the Sigmoid Trick, a novel technique that reduces the gradient update of both cases resulting in better retrieval efficacy. Experiments showed the effectiveness of ETR and sigmoid trick as we achieved four state-of-the-art positions on the kilt knowledge benchmark.

Competence-Based Analysis of Language Models

Despite the recent success of large pretrained language models (LMs) on a variety of prompting tasks, these models can be alarmingly brittle to small changes in inputs or application contexts. To better understand such behavior and motivate the design of more robust LMs, we propose a general experimental framework, CALM (Competence-based Analysis of Language Models), where targeted causal interventions are utilized to damage an LM's internal representation of various linguistic properties in order to evaluate its use of each representation in performing a given task. We implement these interventions as gradient-based adversarial attacks, which (in contrast to prior causal probing methodologies) are able to target arbitrarily-encoded representations of relational properties, and carry out a case study of this approach to analyze how BERT-like LMs use representations of several relational properties in performing associated relation prompting tasks. We find that, while the representations LMs leverage in performing each task are highly entangled, they may be meaningfully interpreted in terms of the tasks where they are most utilized; and more broadly, that CALM enables an expanded scope of inquiry in LM analysis that may be useful in predicting and explaining weaknesses of existing LMs.