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Found 10 papers, 2 papers with code
Understanding and Improving Information Preservation in Prompt Compression for LLMs
Recent advancements in large language models (LLMs) have enabled their successful application to a broad range of tasks.
Open Domain Question Answering with Conflicting Contexts
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions.
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models
The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone.
General Purpose Verification for Chain of Thought Prompting
Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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