Revealing the Unwritten: Visual Investigation of Beam Search Trees to
Address Language Model Prompting Challenges
- URL: http://arxiv.org/abs/2310.11252v1
- Date: Tue, 17 Oct 2023 13:20:16 GMT
- Title: Revealing the Unwritten: Visual Investigation of Beam Search Trees to
Address Language Model Prompting Challenges
- Authors: Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova, Tobias St\"ahle,
Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady
- Abstract summary: We identify several challenges associated with prompting large language models, categorized into data- and model-specific, linguistic, and socio-linguistic challenges.
A comprehensive examination of model outputs, including runner-up candidates and their corresponding probabilities, is needed to address these issues.
We introduce an interactive visual method for investigating the beam search tree, facilitating analysis of the decisions made by the model during generation.
- Score: 29.856694782121448
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The growing popularity of generative language models has amplified interest
in interactive methods to guide model outputs. Prompt refinement is considered
one of the most effective means to influence output among these methods. We
identify several challenges associated with prompting large language models,
categorized into data- and model-specific, linguistic, and socio-linguistic
challenges. A comprehensive examination of model outputs, including runner-up
candidates and their corresponding probabilities, is needed to address these
issues. The beam search tree, the prevalent algorithm to sample model outputs,
can inherently supply this information. Consequently, we introduce an
interactive visual method for investigating the beam search tree, facilitating
analysis of the decisions made by the model during generation. We
quantitatively show the value of exposing the beam search tree and present five
detailed analysis scenarios addressing the identified challenges. Our
methodology validates existing results and offers additional insights.
Related papers
- Technical Report: Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research.
We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
arXiv Detail & Related papers (2024-11-18T16:15:17Z) - Likelihood as a Performance Gauge for Retrieval-Augmented Generation [78.28197013467157]
We show that likelihoods serve as an effective gauge for language model performance.
We propose two methods that use question likelihood as a gauge for selecting and constructing prompts that lead to better performance.
arXiv Detail & Related papers (2024-11-12T13:14:09Z) - generAItor: Tree-in-the-Loop Text Generation for Language Model
Explainability and Adaptation [28.715001906405362]
Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation.
We tackle this shortcoming by proposing a tree-in-the-loop approach, where a visual representation of the beam search tree is the central component for analyzing, explaining, and adapting the generated outputs.
We present generAItor, a visual analytics technique, augmenting the central beam search tree with various task-specific widgets, providing targeted visualizations and interaction possibilities.
arXiv Detail & Related papers (2024-03-12T13:09:15Z) - DPP-Based Adversarial Prompt Searching for Lanugage Models [56.73828162194457]
Auto-regressive Selective Replacement Ascent (ASRA) is a discrete optimization algorithm that selects prompts based on both quality and similarity with determinantal point process (DPP)
Experimental results on six different pre-trained language models demonstrate the efficacy of ASRA for eliciting toxic content.
arXiv Detail & Related papers (2024-03-01T05:28:06Z) - A Comparative Analysis of Conversational Large Language Models in
Knowledge-Based Text Generation [5.661396828160973]
We conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples.
We compare four large language models of varying sizes with different prompting techniques.
Our findings show that the capabilities of large language models in triple verbalization can be significantly improved through few-shot prompting, post-processing, and efficient fine-tuning techniques.
arXiv Detail & Related papers (2024-02-02T15:26:39Z) - Evaluating Large Language Models in Semantic Parsing for Conversational
Question Answering over Knowledge Graphs [6.869834883252353]
This paper evaluates the performance of large language models that have not been explicitly pre-trained on this task.
Our results demonstrate that large language models are capable of generating graph queries from dialogues.
arXiv Detail & Related papers (2024-01-03T12:28:33Z) - OLaLa: Ontology Matching with Large Language Models [2.211868306499727]
Ontology Matching is a challenging task where information in natural language is one of the most important signals to process.
With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline.
We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems.
arXiv Detail & Related papers (2023-11-07T09:34:20Z) - RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models [57.12888828853409]
RAVEN is a model that combines retrieval-augmented masked language modeling and prefix language modeling.
Fusion-in-Context Learning enables the model to leverage more in-context examples without requiring additional training.
Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning.
arXiv Detail & Related papers (2023-08-15T17:59:18Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - Probing via Prompting [71.7904179689271]
This paper introduces a novel model-free approach to probing, by formulating probing as a prompting task.
We conduct experiments on five probing tasks and show that our approach is comparable or better at extracting information than diagnostic probes.
We then examine the usefulness of a specific linguistic property for pre-training by removing the heads that are essential to that property and evaluating the resulting model's performance on language modeling.
arXiv Detail & Related papers (2022-07-04T22:14:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.