12 citations · 33 across the 12 of their papers we have counts for
12 papers
Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Traces
Simon Yu, Derek Chong, Ananjan Nandi +4
As LLM agent systems take on more complex tasks, they increasingly rely on meta-agents: higher-order agents that create, operate on and manage other agents. Meta-agent operations s…
HyperSteer: Activation Steering at Scale with Hypernetworks
Jiuding Sun, Sidharth Baskaran, Zhengxuan Wu +3
Steering language models (LMs) by modifying internal activations is a popular approach for controlling text generation. Unsupervised dictionary learning methods, e.g., sparse autoe…
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Jiuding Sun, Jing Huang, Sidharth Baskaran +4
Mechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts(e.g., the birth year…
The Quest for the Right Mediator: Surveying Mechanistic Interpretability Through the Lens of Causal Mediation Analysis
Aaron Mueller, Jannik Brinkmann, Millicent Li +10
Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc ev…
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…
Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores
Chantal Shaib, Venkata S. Govindarajan, Joe Barrow +4
The diversity across outputs generated by LLMs shapes perception of their quality and utility. High lexical diversity is often desirable, but there is no standard method to measure…
Future Lens: Anticipating Subsequent Tokens from a Single Hidden State
Koyena Pal, Jiuding Sun, Andrew Yuan +2
We conjecture that hidden state vectors corresponding to individual input tokens encode information sufficient to accurately predict several tokens ahead. More concretely, in this…
Evaluating the Zero-shot Robustness of Instruction-tuned Language Models
Jiuding Sun, Chantal Shaib, Byron C. Wallace
Instruction fine-tuning has recently emerged as a promising approach for improving the zero-shot capabilities of Large Language Models (LLMs) on new tasks. This technique has shown…
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
Ji Qi, Chuchun Zhang, Xiaozhi Wang +8
The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior…
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
Bowen Zhao, Jiuding Sun, Bin Xu +9
Web and artificial intelligence technologies, especially semantic web and knowledge graph (KG), have recently raised significant attention in educational scenarios. Nevertheless, s…
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing
Lunyiu Nie, Jiuding Sun, Yanlin Wang +6
The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structur…
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi +5
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and…