212 research outputs found
Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing
We present a novel technique to remove spurious ambiguity from transition
systems for dependency parsing. Our technique chooses a canonical sequence of
transition operations (computation) for a given dependency tree. Our technique
can be applied to a large class of bottom-up transition systems, including for
instance Nivre (2004) and Attardi (2006)
Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains
Although large language models (LLMs) exhibit remarkable capacity to leverage
in-context demonstrations, it is still unclear to what extent they can learn
new concepts or facts from ground-truth labels. To address this question, we
examine the capacity of instruction-tuned LLMs to follow in-context concept
guidelines for sentence labeling tasks. We design guidelines that present
different types of factual and counterfactual concept definitions, which are
used as prompts for zero-shot sentence classification tasks. Our results show
that although concept definitions consistently help in task performance, only
the larger models (with 70B parameters or more) have limited ability to work
under counterfactual contexts. Importantly, only proprietary models such as
GPT-3.5 and GPT-4 can recognize nonsensical guidelines, which we hypothesize is
due to more sophisticated alignment methods. Finally, we find that
Falcon-180B-chat is outperformed by Llama-2-70B-chat is most cases, which
indicates that careful fine-tuning is more effective than increasing model
scale. Altogether, our simple evaluation method reveals significant gaps in
concept understanding between the most capable open-source language models and
the leading proprietary APIs
Conversation Trees: A Grammar Model for Topic Structure in Forums
Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly per- form two tasks: segment a thread into sub- parts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines
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