Papers by Chris Kedzie
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts (2024.acl-long)
Copied to clipboard
| Challenge: | Existing frameworks for the automated evaluation of natural language texts are based on a large language model (LLM) that fails to agree with human judges and is not fully validated by the human judges. |
| Approach: | They propose a large language model (LLM) that generates a distribution over potential responses to assess multiple dimensions of interest. |
| Outcome: | The proposed framework predicts human judges' assessment of user satisfaction on a scale of 1–4 with an RMS error 0.5, a 2 improvement over the uncalibrated baseline. |
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)
Copied to clipboard
| Challenge: | a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely . |
| Approach: | They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair. |
| Outcome: | The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models . |
Detecting Gang-Involved Escalation on Social Media Using Context (D18-1)
Copied to clipboard
Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
| Challenge: | In cities such as Chicago, gang-involved youth have increasingly turned to social media to post about their experiences and intents online. |
| Approach: | They propose a system that uses domain-specific resources and contextual representations of the emotional and semantic content of the user’s recent tweets and their interactions with other users to detect Aggression and Loss in social media posts. |
| Outcome: | The proposed system improves on a large unlabeled dataset and incorporates contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. |
Do Androids Know They’re Only Dreaming of Electric Sheep? (2024.findings-acl)
Copied to clipboard
| Challenge: | Detecting hallucinations in grounded generation tasks is commonly framed as a textual entailment problem. |
| Approach: | They develop probes that are narrowly trained to predict hallucination in a transformer language model. |
| Outcome: | The probes can detect hallucinations at many transformer layers outperforming baselines and human annotators on two out of three generation tasks. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
Copied to clipboard
David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? (2025.emnlp-main)
Copied to clipboard
Yao Dou, Michel Galley, Baolin Peng, Chris Kedzie, Weixin Cai, Alan Ritter, Chris Quirk, Wei Xu, Jianfeng Gao
| Challenge: | Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. |
| Approach: | They propose to use large language models to simulate users for automatic assistant evaluation. |
| Outcome: | The proposed model outperforms human evaluations on two interactive tasks and achieves Spearman’s of 0.7 on both tasks. |
Content Selection in Deep Learning Models of Summarization (D18-1)
Copied to clipboard
| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies (2020.emnlp-main)
Copied to clipboard
| Challenge: | Using task-oriented dialogue generation benchmarks, we compare the effect of four input linearization strategies on controllability and faithfulness. |
| Approach: | They compare the effect of four input linearization strategies on controllability and faithfulness . they also evaluate how a phrase-based data augmentation method can improve performance . |
| Outcome: | The proposed model can generate utterances whose phrases follow the order of the provided plan. |