19 citations · 83 across the 14 of their papers we have counts for
14 papers
In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning
Xiaochuang Han
In this note, we explore inference-time alignment through in-context learning. We consider a vanilla pretrained language model Llama-2 before any fine-tuning and retrieve an averag…
Understanding In-Context Learning via Supportive Pretraining Data
Xiaochuang Han, Daniel Simig, Todor Mihaylov +3
In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood…
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Weijia Shi, Xiaochuang Han, Mike Lewis +3
Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we pres…
David helps Goliath: Inference-Time Collaboration Between Small Specialized and Large General Diffusion LMs
Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov +1
Diffusion-based language models are emerging as a promising alternative to autoregressive LMs: they approach the competence of autoregressive LMs while offering nuanced controllabi…
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang, Shangbin Feng, Tianxing He +3
Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures, such as planning in robotics, multi-hop question answering or knowl…
Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?
Weijia Shi, Xiaochuang Han, Hila Gonen +3
Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, bu…
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control
Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov
Despite the growing success of diffusion models in continuous-valued domains (e.g., images), similar efforts for discrete domains such as text have yet to match the performance of…
ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data
Xiaochuang Han, Yulia Tsvetkov
Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the…
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates
Xiaochuang Han, Yulia Tsvetkov
Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approac…
Fortifying Toxic Speech Detectors Against Veiled Toxicity
Xiaochuang Han, Yulia Tsvetkov
Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestatio…
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov
Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or th…
No Permanent Friends or Enemies: Tracking Relationships between Nations from News
Xiaochuang Han, Eunsol Choi, Chenhao Tan
Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nati…
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Xiaochuang Han, Jacob Eisenstein
Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large co…
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm
Umashanthi Pavalanathan, Xiaochuang Han, Jacob Eisenstein
Wikipedia has a strong norm of writing in a 'neutral point of view' (NPOV). Articles that violate this norm are tagged, and editors are encouraged to make corrections. But the impa…