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Found 21 papers, 4 papers with code
ODIST: Open World Classification via Distributionally Shifted Instances
In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances.
Inference time LLM alignment in single and multidomain preference spectrum
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure.
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.
Active Evaluation Acquisition for Efficient LLM Benchmarking
Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples based on the outcomes of the selected ones.
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.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
Diable: Efficient Dialogue State Tracking as Operations on Tables
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
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.
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11
With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states.
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
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)
+3
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning
Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot.
Ranked #2 on
Emotion Recognition in Conversation
on EC
Siamese Networks for Semantic Pattern Similarity
Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e. g., preposition or frame).
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction
This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related experiments.
The Sentimental Value of Chinese Sub-Character Components
Sub-character components of Chinese characters carry important semantic information, and recent studies have shown that utilizing this information can improve performance on core semantic tasks.
