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Found 13 papers, 7 papers with code
Social Skill Training with Large Language Models
People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life.
Unintended Impacts of LLM Alignment on Global Representation
Before being deployed for user-facing applications, developers align Large Language Models (LLMs) to user preferences through a variety of procedures, such as Reinforcement Learning From Human Feedback (RLHF) and Direct Preference Optimization (DPO).
A Material Lens on Coloniality in NLP
Coloniality, the continuation of colonial harms beyond "official" colonization, has pervasive effects across society and scientific fields.
Task-Agnostic Low-Rank Adapters for Unseen English Dialects
Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English.
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners.
DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules
We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.
Can Large Language Models Transform Computational Social Science?
We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e. g., explaining the underlying attributes of a text).
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands.
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks.
Multi-VALUE: A Framework for Cross-Dialectal English NLP
First, we use this system to stress tests question answering, machine translation, and semantic parsing.
Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers
However, as a fixed-size model acquires more languages, its performance across all languages degrades, a phenomenon termed interference.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.
Ranked #1 on
Event Coreference Resolution
on Gun Violence Corpus
coreference-resolution
Entity Cross-Document Coreference Resolution
+2
The Effectiveness of Simple Hybrid Systems for Hypernym Discovery
Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods.
