ncorporated with CSV, the model becomes capable of usingcode to verify answers

Generating code in brief and frequent segments

can improve computational capability morethan the natural language chains CNL

This study provides the first systematic analysis of code generation, execution, and self-debugging’s role in mathematical problem-solving.

in one round,

The results clearly demonstrate the capa-bility of CoRe to greatly boost PLMs’ reasoningability.

In summary, the overall training objective forverifiers is given by

his helps explain the effectiveness of our method.

Figure 1: Running Examples of Evol-Instruct.

Our GPT4Tools stands distinct from previous and concurrent studies [5– 11] in three ways

Percentage (%) of full-mark answers on LMExamQA

Overview of our benchmarking method.

the visual neck of LaVIN is 6 timessmaller than that of LLaVA [18],

n the image encoder, we insert the adapters beforethe multi-head attention modules.

the number of optimized parameters is still kept at a very small scale, e.g.,3∼5M

Mixture-of-Modality Adapter (MM-Adapter)

can maintain the NLP capabilities of LLMs

To amplify the correspondence signal between the input instance and the correct labe

We hypothesize that during inference, LLMslearn the correspondence between answer choice in the instruction (e.g. Determine the speaker ofthe dialogue, "agent" or "customer".) and the label (e.g. agent) from demonstrations

NLI

Sentence Completion

‘no prompt text’ prompt of COSMOS-QA dataset

e model trained on SVIT can describe abundant detailsaccurately

we follow their setting and only feed the detail description subset of SVIT into the model

Errors in original annotations.

his indicates the importance of keeping the trainingdata diverse and balanced across different categories in IFT

∼6k high-quality data suffices to finetune LLaMA achieving similar performance as theoriginal ALPACA

ALPAGASUS trained on 3k/6k/9k selected data.

we designate “accuracy” as the dimension for ratingpurposes

nearest neighbour score, which is a metric ofdataset diversity

GRIPSworks best when models can follow declarativeinstructions and are responsive to changes to in-structions (shown in Appendix D)

Wedefine the partial order between y1 and candidatesbehind it as y1,2:n = y1 ≻ {y2, · · · , yn}, then theobjective of Bradley-Terry becomes

Step-aware Verifier

Step-aware Voting Verifier

We regard thereasoning paths that match the ground truth finalanswer as positive, and the others as negative.

feedback-based imitation learning

several advantages

The modest performance gains in Math Reasoning can be traced back to the inability to accuratelyidentify whether there is any error.

This step is not intended to teach the generator new skills; it is intendedonly to teach the generator to produce solutions in the desired format

It provides more precise feedback,since it specifies the exact location of any errors that occur.

dropout significantly improves solution-level verifiers

Unfortunately, test@100 performance degradesmuch more sharply than test@1 as we increase the number of epoch

Supervised finetuning

PAIRRANKER outperforms other rankers.

since a single logical error is enough to derail amuch larger solution

showing that they do not introducedomain-label bia

we use random words sampled from the un-labeled evaluation dataset as the content-free text

Domain Label Bias

Us-ing random words limits the semantic meaning ofthe input, allowing us to estimate the vanilla-labeland context-label biases while using in-domainwords accounts for the effect of the task corpus

On the other hand, the models do not improve (or even decline in accuracy) on evaluation datasets forwhich there is little support.