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Found 8 papers, 2 papers with code
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text.
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.
Anticipating the Unseen Discrepancy for Vision and Language Navigation
In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.
MIC: Model-agnostic Integrated Cross-channel Recommenders
However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.
Answering Any-hop Open-domain Questions with Iterative Document Reranking
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.
Ranked #16 on
Question Answering
on HotpotQA
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e. g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
Glyce: Glyph-vectors for Chinese Character Representations
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on
Chinese Sentence Pair Classification
on LCQMC
Chinese Dependency Parsing
Chinese Named Entity Recognition
+21
