REVIEW 5 cited by
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning
read the original abstract
Language models struggle with handling numerical data and performing arithmetic operations. We hypothesize that this limitation can be partially attributed to non-intuitive textual numbers representation. When a digit is read or generated by a causal language model it does not know its place value (e.g. thousands vs. hundreds) until the entire number is processed. To address this issue, we propose a simple adjustment to how numbers are represented by including the count of digits before each number. For instance, instead of "42", we suggest using "{2:42}" as the new format. This approach, which we term NumeroLogic, offers an added advantage in number generation by serving as a Chain of Thought (CoT). By requiring the model to consider the number of digits first, it enhances the reasoning process before generating the actual number. We use arithmetic tasks to demonstrate the effectiveness of the NumeroLogic formatting. We further demonstrate NumeroLogic applicability to general natural language modeling, improving language understanding performance in the MMLU benchmark.
Forward citations
Cited by 5 Pith papers
-
ArgBench: Benchmarking LLMs on Computational Argumentation Tasks
ArgBench unifies 33 existing datasets into a standardized benchmark for testing LLMs across 46 argumentation tasks and analyzes the impact of prompting techniques and model factors on performance.
-
Efficient numeracy in language models through single-token number embeddings
BitTokens represent numbers as single tokens via IEEE 754 binary format, allowing small language models to learn basic arithmetic algorithms nearly perfectly.
-
Strefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Data
Adding Strefer's synthetic space-time reference questions to video instruction tuning improves mask-referred description/QA, timestamp QA, and temporal reasoning over a video-LLM baseline.
-
A Triadic Suffix Tokenization Scheme for Numerical Reasoning
Triadic Suffix Tokenization groups digits into triads with fixed magnitude suffixes to make order-of-magnitude relationships explicit at the token level for LLMs.
-
BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
BALM-TSF combines a statistical-prompt text branch with a patch-based time series branch, using scaling plus contrastive alignment to balance the two modalities, improving long-term and few-shot forecasting on five of...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.