Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
Authors: Tianci Liu
Ruirui Li
Yunzhe Qi
Hui Liu
Xianfeng Tang
Tianqi Zheng
Qingyu Yin
Monica Xiao Cheng
Jun Huan
Haoyu Wang
Jing Gao
Venue: ICLR 2025 Poster (2025)
Keywords: Knowledge Editing
Representation Fine-tuning
Large Language Models
Primary area: foundation or frontier models, including LLMs

Abstract: Large language models (LLMs) have achieved remarkable performance on vari-
ous natural language tasks. However, they are trained on static corpora and their
knowledge can become outdated quickly in the fast-changing world. This moti-
vates the development of knowledge editing methods designed to update certain
knowledge in LLMs without changing unrelated others. To make selective edits,
previous efforts often sought to update a small amount of parameters in some spe-
cific layer(s) of a LLM. Nonetheless, in challenging scenarios, they still fall short
in making successful edits while preserving knowledge irrelevant to the updates
simultaneously, resulting in a notable editing-locality trade-off. In this work, we
question if the trade-offs are caused by the fact that parameter-based updates have
a global effect, i.e., edited parameters affect all inputs indiscriminately. In light of
this, we explore the feasibility of representation fine-tuning, which applied some
linear update to a few representations in a learned subspace, for knowledge edit-
ing. While being effective to enhance an LLM’s general ability as demonstrated in
the previous work, we theoretically show that this linear update imposes a tension
in editing-locality trade-off. Subsequently, BaFT is proposed to break the linear-
ity. BaFT computes a weight for each basis that spans a dimension of the subspace
based on the input representation. This input-dependent weighting mechanism al-
lows BaFT to manage different types of knowledge in an adaptive way, thereby
achieving a better editing-locality trade-off. Experiments on three LLMs with five
editing benchmarks in diverse scenarios show the superiority of our method.

Published as a conference paper at ICLR 2025
UNLOCKING EFFICIENT, SCALABLE, AND CONTIN- UAL KNOWLEDGE EDITING WITH BASIS-LEVEL REP- RESENTATION FINE-TUNING
Tianci Liu', Ruirui Lie, Yunzhe Qi', Hui Liu', Xianfeng Tang", Tianqi Zheng", Qingyu Yin?,
Monica Cheng?, Jun Huan', Haoyu Wang Jing Gao
!Purdue University 2 Amazon UIUC
AWSS Al Lab
SSNY Albany
{[1iu3351, jinggao)@puzzue..dd
2 2ruirul @ amazon, com
Sawang280alband edu
ABSTRACT
Large language models (LLMs) have achieved remarkable performance on vari- ous natural language tasks. However, they are trained on static corpora and their knowledge can become outdated quickly in the fast-changing world. This moti- vates the development of knowledge editing methods designed to update certain knowledge in LLMs without changing unrelated others. To make selective edits, previous efforts often sought to update a small amount of parameters in some spe- cific layer(s) of LLM. Nonetheless, in challenging scenarios, they still fall short in making successful edits while preserving knowledge irrelevant to the updates simultaneously, resulting in notable editing-localityy trade-off. In this work, we question if the trade-offs are caused by the fact that parameter-basee updates have global effect, i.e., edited parameters affect all inputs indiscriminately. In light of this, we explore the feasibility of representation fine-tuning, which applied some linear update to a few representations in a learned subspace, for knowledge edit- ing. While being effective to enhance an LLM's 's general ability as demonstrated in the previous work, we theoretically show that this linear update imposes a tension in dditing-localit trade-off. Subsequently, BaFT is proposed to break the linear- ity. BaFT computes a weight for each basis that spans a dimension of the subspace based on the input representation. This input-dependent weighting mechanism al- lows BaFT to manage different types of knowledge in an adaptive way, thereby achieving better siitinggloccaity trade-off. Experiments three LLMs with five editing benchmarks diverse scenarios show the superiority of our method.
INTRODUCTION
Language models (LMs) parameterized by deep neural networks (Vaswani et al., 2017; Lewis et al., 2019; Radford et al., 2019; Brown al., 2020) have thrived in producing fluent and meaningful texts on diverse natural language generation and classification tasks (See et al., 2019; Raffel et al., 2020; Ji al., 2023). These successes underscore the versatility of LMs, establishing them as the foundations for different natural language processing applications (Bommasani et al., 2021; Zhou al., 2023). Additionally, with model sizes continually increasing. large language models (LLMs) have demonstrated unprecedented abilities to follow natural language instructions (Dong et al., 2022b; Ouyang et al., 2022), empowering zero-shot adaptations to unseen tasks (Kojima al., 2022), and paving the way towards artificial general intelligence (Bubeck et al., 2023).
Despite their remarkable performance, the real-world deployment of LLMs remains largely unre- solved. While LLMs can understand wide range of contexts, they can only provide feedback based on the static knowledge from the data on which they were trained. In a fast-changing world, most knowledge quickly becomes outdated. This could amplify critical issues such making factual fallacy (De Cao et al., 2021) or producing harmful generations (Hartvigsen et al., 2022).
As remedy, knowledge editing, whose goal is to update LLM with some specific new knowledge without hurting irrelevant knowledge, has been proposed ( (Wang et al., 2023; Zhang et al., 2024b). Early effort of full fine-tuning proved ineffective as it also disrupted irreeevan knowledge (Wang

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Published as a conference paper at ICLR 2025
et al., 2023), leading editing-locality trade-off. Here locality refers to the ability to main- tain the knowledge that is irrelevant to the updates. To achieve a good locality, the model update needs to be selective and should rely on a small number of parameters ( (Wang et al., 2023), and thus parameter-effiiient fine-tuning (PEFT) methods like AdaL.oRA (Zhang et al., 2023) have shown good performance (Wu et al., 2023). On the other hand, Huang et al. (2023); Dong et al. (2022a) stricted updates to specific ferd-forward network (FFN) layer that served for knowledge storing (Dai al., 2021). Meng et al. (2022a:b) refined the process through a locate-and-edil paradigm which involves an additional locating stage to identify which layer the target knowledge is stored. Nonethe- less, these methods still exhibit a certain editing-locality trade-off, regardless of whether locating is performed. We note that these methods are parameter-based and have a global effect, i.e., the edited parameters affect all inputs indiscriminately. This observation challenges to what extent an editing can truly benefit from the targeted effort to identify "better" parameters that "memorize" certain knowledge (Hase ct al., 2024). In other words, it is an open question if such trade-offs are due to the coarse control of global parameter-basee updates.
This paper, following Hernandez et al. (2023) that modifies LLM knowledge by updating representa- tions, explores selective representation-basse knowledge editing, and paves way for an affirmative answer to the above question. Our work is built upon ReFT (Wu et al., 2024) that fine- tunes a few representations in a low-rank linear subspace, and performs on par with PEFT methods such as LoRA family (Hu et al., 2021; Zhang et al., 2023; Ding et al., 2023) and others (Houlsby et al., 2019: Chen al., 2024). Unlike parameter-basee updates that apply to all inputs. ReFT only alters representations at some locations. Consequently, ReFT can achieve better editing-locality trade-off than parameter-based updates. Notwithstandinng in spite of this promising result, the subspace-leve linearity still restricts ReFT from providing precise enough updates for knowledge editing.
Specifically, ReFT applies the linear update in the subspace for all selected representations. While being effective to enhance an LLM's general ability such as commonsense reasoning (Wu et al., 2024), this subspace-level control can be too coarse for knowledge editing. As a consequence, when ReFT achieves high editing performance, certain unrelated knowledge may be modified incorrectly, provably jeopardizing its locality. This insight is formalized in Sec 2, where theoretical analysis on this inherent tension is derived, based two reasonable assumptions on how representations convey different knowledge. Notably, our analysis reveals an intrinsic limitation of linear representation fine-tuning It not only holds for knowledge editing, but also applies to other tasks that require selective updates such as continual learning and machine unlearning, and can be of independent interest to these communities. This theoretical result one of the main contributions of this paper.
In light of this insight, we derive BaFT, a more precise representation fine-tuning method for knowl- edge editing. Noting that the subspace is spanned by a group of bases vectors, BaFT instead learns basis-level update. This involves computing a weight for each basis for a given representation, then learning linear update along this basis. Since each basis spans a rank-1 subspace, BaFT is a gen- eralization of ReFT, in the sense that if all bases use the same constant weight 1, BaFT reduces to ReFT. By using different weights combinations on distinct types of knowledge, BaFT can manage them in more adaptive way. When auxiliary locality information (e.g.. what knowledge should not be updated) is available, BaFT can freely restrict the impact of unimportant bases only, while ReFT needs to regulate the whole subspace rigidly. This flexibility makes BaFT highly suitable for knowledge editing and performs on par with the strongest baseline that relies on external memories to memorize new knowledge and requires 10-20 times more parameters. In conclusion, BaFT, new representation fine-tuning method, successfully reaches a better dliting-localii trade-off while maintaining the parameter efficiency of ReFT. This is another main contribution of this work.
Our paper is organized as follows. Sec 2 details the proposed BaFT. Extensive experimental results in Sec 3 demonstrate the superiority of our method for conducting knowledge editing much better parameter efficiency than existing methods. In the remaining part of this paper, we review related works in Sec 4, and conclude the paper in Sec 5.
2 PROPOSED METHOD
Grounded in a theoretical analysis, we show that the linearity nature of existing representation fine- tuning method induces an inherent limitation on its locaing trade-off. We then propose BaFT towards a fine-grained controlled representation fine-tuning in accordance with knowledge editing.
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2.1 PRELIMINARIES
Given input æ = (zi,.... Ln), where each x; E V is a token from vocabulary V, a language model (LM) parameterized by 0 assigns probability po( using the chain rule (Bengio al., 2000):
(e = I po(a, æ1. L4 ==1
II pa(a; x<)).
where pa(a; | (<()) is the predicted distribution of the next token I; over V given previous 2zi. In specific, for L-layer LM, let h. denote the intermediate representation of the i-th token the 1-th layer. The predicted distribution is given by softmax regression parameterized by W at layer L:
po xxa))= softmax (Wh!.)) (1)).
To generate sentence æ, the LM repeatedly computes (2, x<1I) and draws z; from it: then x; fed back into the LM as part of the inputs for future steps. The generation process completes if special token that marks the end of the sentence is returned, the maximum length is reached.
Knowledge Editing aims to incorporate new provided knowledge into a pre-trained LM while pre- serving other existing knowledge that shouldn't be modified. Formally speaking, any knowledge can be represented in natural language with a textual pair (z. y), where æ entails some subject and relation, and refers the corresponding object. For instance, given æ being The current president of United States is, y can be Joe Biden. Knowledge editing seeks to maximize the chance of an I LLM responding with y given æ, while satisfying the following additional criteria at the same time (Zhang et al., 2024b; Liu et al., 2025a): (1) Generality: there are different ways to express US president, wherefore the edited model should generalize. (2) Portability: relevant knowledge such the first lady of United States should be updated as well. (3) Locality: irrelevant knowledge such as the prime minister of United Kingdom should not be affected. Notably, such requirements of modifying only specific internal knowledge in LM has been proved challenging. As revealed in previous works (Zhang et al., 2024b), this process should update only a minimal amount of parameters.
Representation Fine-tuning (ReFT), proposed by Wu et al. (2024), is recent parameter-efffcient fine-tuning (PEFT) method that outperformed other approaches such as LoRA in updating pre- trained LM on several tasks with much less parameters. Building upon the so-called linear represen- tation hypothexis (Park et al., 2023) which presumes that concepts are encoded in linear subspace of representations, ReFT learns low-rank linear updates representations, In particular, to update the d-dimensional representation h at layer for the i-th token, ReFT learns
(c(
;
(1) (r) (1) h
(Aih (1) bi R.h("),
where Φi (Ra, Ar, br) are learnable parameters added to layer l. Here R2 R a low-rank matrix (i.e., < d) containing mutually orthogonal rows that specifies subspace to make the update, and (A,, by) predicts the updated representation this subspace. Finally, ReFT requires hyper-parameter I C [n] to specify which locations need updates. Put together, ReFT intervenes the layer I's output by
h (1) + (or(hi if i E else h (
VEL.....
From now on, we omit indices i, when discussing how representation h is intervened for brevity.
2.2 EDITING KNOWLEDGE BY FINE-TUNING REPRESENTATIONS
ReFT has demonstrated impressive performance on tasks such as commonsense reasoning that largely rely on an LLM's ability to understand and generate text by updating just a few (i.e., those in I) representations. However, is unknown whether this lightweight approach can benefit knowl- edge editing, which requires modifying some selective internal knowledge. Here, we show that the linearity nature of ReFT limits its editing and locality performance. specific, for all inputs, ReFT applies the same linear update without distinction:
(h) h R (Ah + b Rh)- (I+R ( (A-R)
R R
weight
bins
The coarse control from the linear ReFT makes it less suitable for knowledge editing for two reasons.
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First, ReFT uses its learned subspace for editing in prede- termined manner, regardless of varying levels of learning dif- ficulty for different types of knowledge. This can lead to sub- optimal performance. As an evidence, we fit a rank-12 12 sub- space for ReFT and checked how many dimensions (bases) contribute negligible updates, as measure of dimension re- dundancy. To this end, we count for each dimension, if its update magnitude is less than M times of the maximal dimen- sion. Fig shows these results. We noted that the dimension redundancy indeed varies on different types of knowledge.
Figure Averaged (w/ max-min range) number of redundant dimen- sions (which have update M times smaller than maximal values), in rank-12 ReFT update.
Second, the linearity of ReFT leads to an inherent editing- locality trade-off: it is challenging to maintain good general- ity and locality at the same time. Formally, given some knowl- edge involves subject relation r, and object that can be updated by ReFT, we make the following assumptions.
Assumption 2.1. Let text encodes s. Since the knowledge can be edited by ReFT, text generated by the LM will convey o its intermediate representation takes some targeted value Assumption 2.2. (Hartvigsen et al., 2024) For any h carrying some knowledge, there exists pos- itive e(h)-radiux & ball Bh,, (())) around such that any h' E B(h, conveys the same knowledge, we refer to ((h) as stable-ball of h.
We provide few clarifications on the two assumptions. The first assumes that a piece of knowledge can be generated (retrieved) from some associated representation. The second, as in Hartvigsen et al. (2024), assumes that the knowledge is locally stable around its representation, so that a small perturbation won't change the carried knowledge. Under these two assumptions, The following Thm 2.3 reveals tension between maintaining good generality and locality simultaneously, with its proof deferred to App B.I.
Theorem 2.3. When fine-tuning an LM, ReFT learns to update the old representation ho to targeted t = (ha)) If ReFT maintains good generality such that V h E B(h, (ho)).
[1+(()) T (hho)ll = (b) tll < e(t),
where ||
denote the f2 norm. Then for any irrelevant imput hir with small stable-ball radius
|t holl (=(t) + =(ho)) s(ho).
<(hir)
E(t) + 25 (ho)
and is not too far from bg such that
|i boll a =(hrr) + E(to), ReFT will output (hh,)) & B B(hir, e (har)) and break its locality guarantee.
Intuitively speaking. Thm 2.3 formalizes that ReFT update has be large enough to make successful edit; and smooth enough to achieve good generality. Then, due to its linearity, it will inevitably hurt the locality of some irrelevant knowledge. This limitation does not rely on the specific r (i.c., subspace rank) being used. In summary, ReFT is less suitable for knowledge editing because of the two limitations, which motivates BaFT as presented in the next section.
2.3 BAFT: BASIS- REPRESENTATION FINE-TUNING
Given the limitations from linearity, i.e. using the whole linear subspace update all repre- sentation without distinction, and the finding of dimension (basis) redundancy, we propose to take the importance of each dimension into account. Since in ReFT, the subspace is parameterized by a set of orthogonal bases vectors, we assign each basis learnable weight to determine how much contributes to the current editing. This
weighting mechanism makes our method applies a non-linear update. We dub our method basis-leve representation fine-tuning (BaFT).
To be more specific, a layer where ReFT takes place, we learn an r-dimensional update by
(hh) h +
we we(h)rk(a h by - r((h). h),
(2)
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where ri....,, Fy are d-dimensional orthogonal bases, Q1..... Fy and bi...., b, are arbitrary vectors and scalars, respectively. Finally, (y)h) E [0, 1] are r learnable weights. Put together, wy (a) h by r h) predicts the magnitude of update along direction of basis ra, and BaFT combines r total updates to form the final intervention. Fig 4 illustrates the overall flow of BaFT.
While appears distinct, Lem 2.4 shows that BaFT generalizes ReFT. See its proof in App B.3.
Lemma 2.4. Let R = [rii...; [Tr] E IRxx A [al,..., ar) E Brxd, bt = (bi,...,buy bR), and W(h) diag(w) (wi (h):,... ..... Wy (h)) be a diagonal matrix. BaFT in Eqn (2) can be expressed as
+ (h) h + R (h) (Ah + b- 10--RR Rh).
When using constant weighting W(h) BaFT reduces ReFT.
2.4 TRAINING OBJECTIVE OF BAFT
We end this section by detailing the training of BaFT. For consistency we use d) to denote the collection of learnable parameters layer R, A, b, and newly introduced parameters in W. Given set of pre-specified layers C that need interventions, we optimize the collection of all learnable parameters Ø { Jee, using the following losses.
Seacher-forcing Loss. Following Wu et al. (2024), we train BaFT with language modeling objec- tive, and minimize the cross-entropy loss with teacher-forcing (Lamb et al., 2016) at output positions
L (Ø) &
Po zy<s (),
where the intervention is applied to the last P positions in æ, together with all entries in y.
Incremental Load Balancing Loss. When editing multiple pieces of knowledge, different bases need, average, balanced weights. Otherwise, using few fixed bases for all edits is equivalent to using a fixed subspace spanned by these bases, and BaFT will reduce to a smaller ReFT. To avoid this reduction, inspired by the sparse mixture of expert (Shazeer al., 2017: Fedus et al., 2022), regularize the squared coefficient of variation of (wi (h),... .................. wi, (h)). However, as new knowledge may emerges one by one, making direct average over multiple samples infeasible, we compute the metric in an incremental way. Namely, when editing the (-th knowledge, we minimize
Real (Ø) (w))()) - w(1)²² (r- l)m(t)
where w(t) =
Ty (t),
k=1
and (t)(t) averages weights Wy over the current and past training samples at selected positions. For incremental optimization, only minimize (o)( with respect to the current weight on the f-th knowledge, as highlighted by expressing (t) as a function of current step t.
Locality Regularization. In some scenarios, it is feasible obtain examples of irrelevant knowl- edge during training ( (Wang et al., 2024d; Yu et al., 2024). Such information can benefit the training of BaFT well. Following Wang al. (2024d), we incorporate the margin loss regularizer. Let and hir denote the representations of editing and irrelevant knowledge, respectively, we minimize
Rioe (Ø) m0, ((h a) max(0, 3 W(h) + max (W m max W(h
weight (ht),) S a
edit. weight un (h) 8
edit weight loc weight
At a colloquial level, Ro) encourages that weights for irrelevant knowledge should be as small as editing knowledge's weight should be no less as 3, and at the same time, the most important weights from the two groups should have a gap that is as large as T.
In execution, rescale the three terms to the same magnitude and solve the following objective
min L e min (1) + Real + R1o((0).
(4)
ReFT, special case of BaFT, only minimizes I. (g).
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3 EXPERIMENT
We test the proposed BaFT for knowledge editing on three 7B-level autoregressive language models (LMs) over five public benchmarks. Ablation studies are also conducted. Experiment results show that BaFT can achieve excellent performance at much better parameter efficiency.
3.1 EXPERIMENT SETUP
Base Models. We conduct experiments on three representative LLMs from different model families. LLaMA 2-7b (and LLaMA 2-7b-Chat) (Touvron et al., 2023) have been widely studied in the literature (Zhang et al., 2024b; Wang et al., 2024d) and we follow this convention. Trending LLaMA 3-8b-Instruct (Dubey et al., 2024) and Gemma I.1-7h-Instruct (Team al., 2024) are also studied. From now on, refer to the three LLMs as LLaMA 2(-chat), LLaMA 3, and Gemma for brevity.
Tasks. Following previous works (Wang et al., 2023; Zhang et al., 2024b), we edit different kinds of knowledge: WikiDatareennt WikiDatt (Cohen et al., 2024), WikiBio (Hartvigsen et al., 2024), ConvSent (Mitchell et al., 2022), and ZsRE (Yao et al., 2023). Due to page limitation, we refer readers Zhang al. (2024b) for more benchmark details. When editing LLM, three scenarios are considered. Single Editing updates one piece of knowledge at a time. Continual Editing and Batched Editing, the other hand, update multiple pieces of knowledge in sequen- tial or batched way. The two latter are more challenging due to potential forgetting and knowledge conflicting problems, as observed in the literature (Hartvigsen et al., 2024; Wang al., 2024d).
Baselines. We follow Zhang et al. (2024b); Wang et al. (2024e) and choose AdaLoRA (Zhang al., 2023), ROME and FT-L (Meng et al., 2022a), and MEMIT (Meng et al., 2022b) as base- lines. In continual editing scenarios. we further include representative memory based methods GRACE (Hartvigsen al., 2024), MELO (Yu al., 2024), and WISE (Wang et al., 2024d). All these baselines, same as ours, do not require larges-scale hard-lo-access training data, or training additional models: Ada learns low-rank update for model parameters on the new knowl- edge while kceping less important parameters unchanged, thereby achieving highly efficient and precise PEFT. ROME applics a tracinn analysis to identify the layer wherein the knowledge stored and then solves analytic rank-one update. FT-L, the other hand, directly finetunes the layer identified by ROME with an additional KL. divergence loss. MEMIT extends ROME to a batched editing setting by identifying series of layers edit and finding the updates as least squares solutions. GRACE, MELO, and WISE are specialized for continual editing. They leverage side pa- rameters save new knowledge and learn gating mechanism to determine whether pre-trained or new knowledge should be used during inference. Finally, we include ReFT as a baseline that uses a subspace of the same rank as BaFT.
Evaluation Criteria. We evaluate the performance from multiple aspects (Zhang et al., 2024b; Wang et al., 2024d). Given an edited model, reliability (Rel.) evaluates whether it successfully learss the new knowledge; generality (Gen.) measures to what extent it can generalize to rephrased knowledge inquiries; locality (Loc.) quantifies how much the model can retain its original output on irrelevant knowledge inquiries; portability (Por.) checks if the model is able to transfer new knowl- edge to related content. We report the average different metries for more complete comparisons.
Implementation Details. Our experiments are conducted with FasyEdit (Wang et al., 2024e). More implementation details and sypee-parrmetter can be found in App C.
3.2 SINGLE EDITING PERFORMANCE
We evaluate the effectiveness of the proposed BaFT for conducting Single Editing on WikiDatarcentt WikiDatt WikiBio, and ConvSent (only supports LLaMA family). The four benchmarks do not contain irrelevant data. Consequently, BaFT training does not involve the locality regularization.
Single Editing results reported in Tab 1. The proposed BaFT performs highly competitively in all cases. BaFT and ReFT use a subspace of the same rank to edit representations, so an ideal BaFT should achieve reliability comparable to ReFT that edit representations freely. Indeed, BaFT maintains a better editing-locality trade-off: it consistently achieves better locality and portability
Not all benchmarks support all metrics.

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Published as a conference paper at ICLR 2025
than ReFT with no degradation of reliability. In comparison, other baselines suffer from notable editing-locality trade-off, i.e., achieve high reliability at a price of low locality. These methods also exhibit significant performance gaps when editing different LLMs. These results demonstrates BaFT as a new promising editing solution.
Table 1: Single Editing performance on four benchmark datasets. Results marked with "P" are taken from Zhang et al. (2024b). Unsupported experiments are marked with "x" Best Avg. results are in bold and second best are underlined.
WikiBio
ConvSent
LLsMA
2-7b-chat
Rel.
Por:
Loc.
Avg.
Rel.
Por:
Loc.
Avg.
Rel.
Loc.
Avg.
Rel.
AdaLoRA
0.56
0.74
0.70
0.91
ITL
0.56
0.41
0.44
0.47
0.45
0.34
0.50
0.51
0.66
0.80
0.73
0.50
ROMER
0.55
0.99
0,52
0.80
MEMIT
0.97
0.56
0.52
0.68
0.98
0.59
0.47
0.68
0.94
0.62
0.78
0.45
ReFT
1.00
0.60
0.71
0.77
1.00
0.72
0.78
0.83
1,00
0.91
0.96
1.00
BaFT (Ours)
1,00
0.61
0.73
0.78
1.00
0.72
0.81
0.84
1.00
0.94
0.97
1.00
LLaMA 3-80-Instruet
Rel.
Por:
Loc.
Avg.
Rel.
Por:
Loc.
Avg-
Rel.
Loc.
Avg.
Rel.
AdaL.oRA
1.00
0.61
0.45
0.69
1.00
0.74
0,51
0.75
1.00
0.79
0.90
1.00
0.22
0.64
ROME
0.99
0.58
0.49
0.69
0.99
0.58
0.41
0.66
0.92
0.68
0.80
0.98
MEMIT
0.99
01.54
0..48
0.67
0.99
0.58
0.43
0.67
0.96
0.71
0.84
0:32
ReFT
1.50
'0.62"
0066
0.75
1100
0.72
0.74
6.82
1.00
'087
0.92
0.96
BaFT (Ours)
1.00
0.62
0.64
0.75
1.00
0.72
0.75
0.82
1.00
0.91
0.96
0.96
Gemma L.I-7-Instruct
Rel.
Por:
1.cc
Avg.
Rel.
Por:
Loc.
Avg.
Rel.
Lnc.
Avg.
Rel.
AdaLoRA
1.00
0.58
0.28
0.62
1.00
0.70
0.35
0.68
1.00
0.70
0.85
0.03
0.19
ROME
0.79
0.38
0.27
0.48
0.82
0.47
0.27
0.52
0.47
0.31
0.39
1.50
"0.54
0.33
0.20
130
0.85
0.12
0.78
T.00
0082
099
BaFT (Curs)
1.00
0.54
0.58
0.71
1.00
0.62
0.77
0.80
1.00
0.85
0.93
X
3.3
CONTINUAL AND BATCHED EDITING PERFORMANCE.
Next, we study the two challenging scenarios, where massive editings are conducted in a sequential (continual) or batched way. We follow Wang et al. (2024d) and experiment with LLaMA (non-chat version), LLaMA 3, and Gemma ZsRE. We note that the state-of-the the- continual editing method WISE contains substantially larger parameter size and is much more computationally expensive. For fair comparison, we include WISF a lightweight version of WISE that contains 1/8 learnable parameters of the original WISE to make its training affordable. We want to highlight that WISE does not change editing mechanism², and still contains more learnable parameters than BaFT and ReFT (10 and 20 times respectively). Learnable parameters used in different methods, along with their time consumptions, are reported in Tab 3.
Continual Editing Performance. Tab 2 presents the main results of continually editing 1000 pieces of ZsRE knowledge. BaFT again achieves remarkable editing performance while maintaining excellent local- ity on LLMs from different families, reaching the best two in nearly all scenarios. In comparison, standard methods AdaLoRA, FT-L, ROME, and MEMIT en- counter considerable performance gaps over different LLMs. Mcanwhile, they fall short in editing multiple pieces of knowledge that emerge sequentially. WISE performs slightly better but its parameter efficiency is much lower, we will show soon. GRACE is de- signed for continual editing but still suffers from fail- ure on editing Gemma These methods might benefit from a more extensive ppper-prrretter tuning for each LLM. Nonetheless, their prolonged running time makes this process expensive, if not unaffordable.
Figure 2: Bases weights used for editing and irrelevant knowledge (averaged over different positions).
WISE finds an important FFN layer to conduct knowledge editing. For each new knowledge, it finetunes small portion of randomly selected parameters in this layer. WISFiihh uses a smaller randomly chosen pool.
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When comparing BaFT and ReFT with each other, we note that in Single Editing, BaFT main- tains, if not surpasses, the editing ability of ReFT. In addition, when the editing number T increases, BaFT shows excellent robustness against forgetting, as indicated by its capability of preserving high locality in all scenarios. We further visualize bases weights in Fig 2, where a one-layer BaFT is used to edit LLaMA 2 on 100 7sRE knowledge with T = 10 (achieved reliability, generality, and locality are 0.75, 0.71, I, and 0.98 respectively). Rel., Gen., and Loc. refers to new, rephrased, and unrelated knowledge, respectively. We note that BaFT evenly distributes the editing over all bases, and unre- lated knowledge receives significantly lower weights. These results confirm that BaFT leverages the fine-grained basis-level control as designed in Sec 2, thereby excelling at Continual Editing.
Table 2: Continual Editing performance on ZsRE dataset, evaluated after conducting T times of editing sequentially, Results marked with "Q" are taken from Wang et al. (2024d). Best Avg. results are in bold and second best are underlined.
T
10
T- 100
7' 1000
LLaMA 2-7b
Rel.
Gian.
Loc.
Avg.
Rel.
Gen..
Inc.
Avg.
Rel.
(ien.
Loc.
Rel.
Gen.
1,c.
Avg.
Adal Mal.
1:00
0.90
0.94
0.38
0.06
0.00
0.00
FTL
0.57
0.55
0.96
0.31
0.26
0.09
0.25
ROME
0.96
0.91
0.98
0.95
0.80
0.76
0.77
0.78
0.18
0.18
0.07
0.12
0.00
0.01
0.00
0.00
MEMIT
0.95
0.90
0.99
0.95
0.77
0.74
0.90
0.80
0.25
0.24
0.19
0.02
0.04
0.04
0.02
0.03
MELO
1.00
0.40
0.99
0.50
0.95
0.10
0.99
0.78
0.61
0.40
0.99
0.67
0.40
0.40
0.99
0.60
GRACE
0.08
0.96
0.00
0.65
0.00
1.00
1.00
WISE,1
0.98
0.92
1.00
0.97
0.94
0.88
1.00
0.94
0.90
0.81
1.00
0.90
0.77
0.72
1.00
0.83
WiSE
0.95
0.83
1.00
0.91
0.93
0.74
1.00
0.83
0.73
0.99
0.85
0.49
0.47
1.00
0.65
ReFT
1.000
0.55
0.34
T096
0.90
0.35
0.87
0.73
0.74
0.35
00.78
0.5h
0.56
0.75
0062
BaFT (Ouns)
1.00
0.94
0.97
0.97
0.89
0.81
0.97
0.90
0.75
0.70
0.98
0.81
0.63
0.60
0.98
0.74
LLaMA 3-80-Instnact
Rel.
Gon
Avg.
Rel.
Genn
Avg.
Gen.
1ac.
Rel.
Gen.
1oc.
Avg.
AdaLoRA
0.26
0.01
FT-L
0.51
0.52
0.68
0.57
0.25
0.20
0.03
0.16
0.19
0.16
0.02
0.12
0.16
0.11
0.01
0.10
ROME
0.99
0.96
0.96
0.97
0.62
0.63
0:42
0.56
0.07
0.07
0.01
0.05
0,03
0.03
0.00
0.02
MEMIT
0.99
0.96
0.98
0.98
0.68
0.66
0.71
0.68
0.03
0.03
0.02
0.33
0.00
0.00
0.00
0.00
MELO
1.00
0.29
0.76
0.30
0.62
0.99
0.00
0.02
0,02
0.57
0:31
0.55
WISE
0.95
0.91
0.99
0.95
0.82
0.76
1.00
0.86
0.63
0.57
1.00
0.73
0.39
0.37
1.00
0.59
ReFT
1.00
0.97
0.93
0.97
0.90
0.84
0.37
0.87
0.68
0.6
0.74
0.68
0.48
0.45
0.64
0:52
(Ours)
0.95
0.82
0.93
0.49
0.93
Gemma
1.1-7b Instruct
Rel.
Gc..
Loc:
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
AdbLoRA
1.00
0.97
0.67
0.88
0.19
0.20
0.18
0.19
0.03
0.03
0.01
0.02
0.00
0.00
0.00
0.30
FTL
0:23
ROME
0.75
0.71
0.88
0.78
8.18
0.18
0.14
0.01
0.00
0.00
MELO
1.00
0.20
1.00
0.73
0:96
0:23
1.00
0.75
0.52
0.26
0.95
0.58
0.26
0.25
0.95
0.49
GRACE
0.39
0.00
1.00
0.46
0.39
0.01
1.00
0.47
0.39
0.01
1.00
0.47
0.39
0.01
1.00
0.47
WISE
0.99
0.95
1.00
0.98
0.90
0.81
0.99
0.79
0.71
0.95
0.52
0.48
0.42
0.98
0.63
ReFT
1.00
0.86
0.91
0.92
6.92
081
0.31
0.85
0.66
0.58
0.60
0.64
0.50
0.46
0.85
0.54
BaFT (Ouns
1.00
0.84
0.94
0.93
0.92
0.50
0.92
0.88
0.70
0.62
0.92
0.75
0.48
0.45
0.92
0.62
Table 3: Parameter size and editing time with an NVIDIA V100 32-GB GPU (averaged over 100 samples). ROME, MEMIT, and GRACE do not contain pre-specified learnable parameters.
LLaMA 2-7b6-chat)
LLaMA 3-80-Instruct
Gemma 1.1-7b-Instruct
# Params.
Time (sec./edit)
# Params.
Time (sec./edit)
# Params.
Time (sec//edit)
AdaLoRA
6,292,224
26.24
5.112,576 58.720.256
28.71
4,817,568
44,24
FT-L
45.088.768
9.73
10.84
75.497.472
11.95
ROME
27.27
/
25.01
52.07
MEMIT GRACE
20.01
25.35
/
34.38
87.08
43.45
WISB
5,636,096
58.00
7,340,032
65.77
9,437.184
20.20
ReFT
393,264
T0.95
393,364
'933
254,960
7.79
BaFT (Qurs)
606,256
13.46
606.256
12.69
454,704
10.13
Batched Editing Performance. We further compare BaFT and ReFT against baselines that admit batched data for editing, namely, AdaLoRA, FT-L, and MEMIT. LMs were edited on ZsRE dataset, and batch sizes were set to 10 and 50 respectively.
We visualize the average of reliability, generality, and locality in Fig 3, and defer the complete results to App D. The proposed BaFT again achieved a great balance between good edit success and high locality, outperforming all baselines in 5 out of 6 scenarios. Surprisingly, when T = 10, LoRA
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and MEMIT were capable of benefiting from editing multiple samples in batch than by one. We conjecture that learning multiple pieces of knowledge in a batch helps mitigate their overfitting on any single knowledge, thereby weakened the forgetting problem to some extent. This finding suggests that caching more knowledge and editing them a batch can be beneficial in some cases.
Parameter Efficiency. Continual and Batched Editing involve learning more knowledge than in Single Editing. As a result, achieving good editing performance while maintaining high parameter efficiency is non-trivial, learn more knowledge. We note that while WiSE achieved comparable performance to BaFT, its parameter efficiency was much lower: on LLaMA 2-7b, the edit success dropped from 0.77 (of WISE) to 0.49 when editing 1000 pieces of knowledge, around 22% lower than BaFT which uses 10 times less parameters, as per Table Similar trends can be found when making comparison with LoRA in Batched Editing scenarios. In conclusion, BaFT is capable of achieving much better parameter efficiency than existing methods.
using fewer parameters increases the workload of each parameter to
fert
(a) LLaMA 2-7b
(b) LLaMA 3-8b-Instruct
(c) Gemma J.1-7b-Instrun
NOMT
WETVEE
Horr
(d) L. 2-7h
(c) I.LMMA 3-8h-Instruct
(f) Gemma -nstru-nsttruct
Figure 3: Batched Editing Performance under sequence length. The first row uses batch size 10 and the second row uses batch size 50.
3.4 ABLATION STUDY
We end this section with an ablation study on BaFT showcase how each component contributes to the final performance. Results from continually editing LLaMA 2-7b with 100 ZsRE knowl- edge are presented in Tab We note that introducing a coarse-grained subspace-level weighting (ss-w) which assigns all bases with the same weight along did not benefit ReFT. Moreover, both locality regularization (Ir) and fine-grained basis-level weighting (ba-w) helped improve locality. Remarkably. the basis-level weighting, observed in all Single Editing scenarios, did not lead to edit performance degradation. Locality regularization, while greatly improved the locality, induced trade-off with editing performance at the same time. Notably, the degradation is amplified when the subspace-level weighting was used, echoing well with our theoretical analysis.
In conclusion, the proposed BaFT makes two improvements over ReFT. First, the weighting offers fine-grained level learning, leading to better locality without hurting editing performance. Second, a fine-grained basis-level control allows one regularize locality by altering only the important parts, leading to a better empirical aditing-locality trade-off.
4 RELATED WORKS
Existing editing methods mainly fall into two classes.

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Published as a conference paper at ICLR 2025
Table 4: Component effects in BaFT.
Internal Storage updates model parameters for the adapta- tion. Early efforts involved fine-tuning a LLM directly but suffered from severe forgetting of original knowledge (Wang et al., 2023). For more precise editing, Zhu et al. (2020) im- posed relaxed &g norm constraint on the parameter updates, and Huang et al. (2023); Dong et al. (2022a) limited the up- dates some specific feed-forward network (FFN) layers, based on findings that knowledge is often stored therein (Dai et al., 2021). For further refinement, the locate-and paradigm (Meng et al., 2022a;b) first identifies the layer storing a specific knowledge, and then modifies its parameters in an analytic form or via least squared solution. On the other hand, PEFT methods such as .oal (Zhang et al., 2023) also provided performance on par with locating- based solutions (Wu et al., 2023; Wang ct al., 2024b). However, these methods are parameter-based and offer a similar level of control, in the sense that all inputs altered in the same way. As result, they suffer from an equal level of editing- -occlitty trade-off (Wang et al., 2023; 2024d). These findings raised a question to what extent knowledge can be accurately attributed to some specific parameters (Hase et al., 2024). Inspired by the recent advance of improving a LLM's general ability such as commonsense reasoning by fine-tuning its representations (Wu et al., 2024), in this work we show that updating representations at only a few locations can provide strong editing performance. By pushing the fine-tuning towards a new basis-level, our BaFT achieved better fine-grained control and superior editing-locality trade-off.
Rel.
Gen.
Loc.
Avg.
ReFT 0.76
0.71
0.84
0.77
+ss-w. 0.74
0.68
0.81
0.74
+ba-w 0.77
0.71
0.86
0.78
+ss-w&Ir 0.67
0.61
0.99
0.76
BaFT 0.73 0.67
0.98
0.79
External Storage resorts external memories without changing original parameters. Methods include meta-learning based MEND (Mitchell et al., 2021) and its multi-task version InstructE- dit (Zhang et al., 2024a), IKE (Zheng et al., 2023) and LTE (Jiang et al., 2024) that bear the simi- larity to Retrieval-Augmenned Generation (Gao et al., 2023; Wang et al., 2024a; Xu et al., 2024; Yu et al.. 2025; Liu et al., 2025b), augmentation based StableKE (Wei et al., 2024), and proxy model based SERAC (Mitchell et al., 2022). Notwithstanding, these methods need large-scaled hard-to- access data to retrieve from (c.g.. IKE, LTE), or to train extra model on (c.g.. MEND, InstructEdit, SERAC). As consequence, they have limited practicality and fall short on Continual Editing that requires frequent updates (Wang et al., 2024d). Recently, methods specialized for Continual Editing were proposed (Hartvigsen et al., 2024; Yu et al., 2024; Wang et al., 2024d). These approaches injected lightweight adapters (Hartvigsen et al., 2024) or weight copies (Wang et al., 2024d) to memorize new knowledge, and learned some gating mechanism to determine whether original or new knowledge to use. Specifically. GRACE (Hartvigsen et al., 2024) maintained code-book to determine which adapter will be used based on representation similarity, and MELO (Yu al., 2024) used dynamic LoRA. WISE (Wang et al., 2024d) learned activation threshold to trigger new learned weights. However, these methods have several limitations. First, they often show unsatisfactory generalizability, as observed in Wang et al. (2024d) and confirmed in experiments. Second, they require prolonged training (and inference) time, due to the need of maintaining non-constant num- bers of external memories. Finally, existing gating mechanisms cannot be learned when multiple pieces of knowledge appear, making them incompatible for Batched Editing. In comparison, BaFT learns a pre-specified set of parameters and lets bases weights play the role of gating. This design makes BaFT suitable for both Continual and Batched Editing. Moreover, as editing and activation conducted in representation subspace, BaFT is able to achieve good generalizability at better parameter efficiency.
5 CONCLUSION AND FUTURE WORKS
In this work, we propose new representation based method towards more efficient knowledge edit- ing. Grounded in a theoretical analysis, we show that updating all selected representations with one linear subspace in a predetermined manner imposes a tension in dditing-localipy trade-off. Subse- quently, BaFT as better solution is proposed. Given representation, BaFT first computes weight for each basis that spans the linear subspace, then conducts a linear update along this basis direction. Because bases weights are determined from the current representation with non-linear functions, BaFT fine-tunes the representation in a non- non-linear way. This fine- -ee-graiied control leads to better per- formance on editing three representative LLMs in various scenarios, on par with outperforming the strongest baselines at much better parameter efficiency. As detailed in App A, there are some limitations in this work, and we plan work in future work.
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ACKNOWLEDGEMENT
This work is supported in part by the US National Science Foundation under grant NSF IIS-2141037. Any opinions, findings, and conclusions or recommendations expressed this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao. Ziwen Xu, Mengru Wang, Shengyu Mao. Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, and Huajun Chen. Easyedit: An easy-to-use knowledge editing framework for large language models, 2024e.
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al. Knowledge editing for large language models: survey. arXiv preprint arXiv:2310.162166 2023.
Zihao Wei, Liang Pang, Hanxing Ding, Jingcheng Deng, Huawei Shen, and Xueqi Cheng. Stable knowledge editing in large language models. arXiv preprint 240N::2402.13044 2024.
Suhang Wu, Minlong Peng. Yue Chen, Jinsong Su, and Mingming Sun. Eva-kellm: A new bench- mark for evaluating knowledge editing of llms. arXiv preprint arXiv:2308, 09954, 2023.
Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D Man- ning. and Christopher Potts. Reft: Representation finetuning for language models. arXiv preprint arXiv:2404.03592, 2024.
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A MORE DISCUSSIONS AND LIMITATIONS ON BAFT
In this section we provide more discussions on the proposed BaFT. Fig demonstrates the workflow of our method. There are also some limitations in this work, and we plan to explore in the future.
P
PR.
Edited representations
BaFT Framework
BaFT update
Subspace
Bases weights
Edit
ReFT: M
NOP
weights
<B0S>
text
LLM (froze) forward
Representations
Figure 4: BaFT learns basis-level weights to edit different representations (highlighted in different colors). When using constant weights, BaFT reduces to ReFT.
First, The empirical success of BaFT was mainly established on standard benchmarks EasyEdit (Wang et al., 2024c), which may not be sufficient to reflect the diverse real-world applications. Second, BaFT generalization of ReFT requires hyper-parameter tuning determine proper positions and layers to add interventions. Our choice was selected based on recommended values from ReFT (Wu al., 2024). We plan explore automating this process by imposing proper sparsity constraints on weights in our future work. Third, the promising performance of BaFT demonstrates its potential for efficient knowledge editing. However, it is still open question appresentationnbasee method is capable of fitting any editing (or updates) learnable for parameter- based methods. other words, unknown there is some knowledge that can be learned by parameter-based method but is unlearnable by updating representations. We plan to explore this direction in our future work.
B OMITTED PROOF
We include omitted proof here.
B. PROOF OF TIM 2.3
We start with restating the two assumptions and the theorem.
Assumption B.1. Let text æ encodes 8, r, text generated by the LM will convey o if its interme- diate representation takes some targeted value
Assumption B.2. For any carrying some high-level knowledge, there exists a positive e e(h)) lg ball B (hh, ((h)) around h such that any h' E (h)) conveys the same knowledge, refer to Bhh, E eh)) as stable-ball of h.
Theorem B.3. When finetuning LM, ReFT learns to update the old representation ho to targeted (ha)) If ReFT maintains good generality such that 's E B(h,,
[[)(()) Tb (ha)l ||| = || (b) tll < <(t).
where |||| denote the lg norm. Then for any irrelevant input h with small stable-ball radius
|t holl (s(t) + +(oo))
E(har)
s(ho).
E(t) + 2=
and is not too far from ho such that
hi - holl = E(hu) + E(ho), ReFT will output & (har) & B(ha, e (har)) and break its locality guarantee.
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Proof. First, since old and new knowledge associates with different objects o, by Asmp 2.2, ho and t must have non-overlapped stable-ball. Otherwise, we can find
h E (ho; e c(ho)) n Bt,, e(t), that preserves the knowledge of both ho and t that are different, which is impossible. This implies
|| - holl <(t) + =(ho).
In addition, by definition of ReFT, we have
- ho = x(ho) - hoo
ho + R (Aho + +b) - ho
R (A ho + R
Hhg + R RR
where in the last step (a), we defined H Next, according to the generality condition, for any B(h,, s(ho)), we have
R (A R) for simplicity.
[1 + R (A R)) ho)l
(I H) (h ho)]
<t(t).
Since can take any direction, we know hg can be an arbitrary vector that has norm no greater than e(ho). Let h ho takes the direction of the first right singular vector, then
(T + H ho)ll = maax [I + H)ll holl < st).
This implies that the operator norm of I H. denoted by max upper bounded by
=(t)
max
H
=(ho)
By triangle inequality of the operator norm (Belitskii et al., 2013), further know
E(T)
max
H
Tmax (I+ H S Fmax (I H) + d (I) - =(ho)
+ 1.
Now, for any irrelevant hig we have
(1xr) hall
Hh .R bll
Hher (t ho) HHhl ||
H(trr - ho) + (t ho)ll
(6)
||(t ho)ll - [H( ho)l
where (a) substitutes
t t- h0 Hhg R b.
and (6) holds from the reverse triangle inequality.
When the irrelevant h has a small stable-ball radius.
e (ha)) < || holl (e(t) + s(ha))
e(ho).
20(ho) + E(t)
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Published as a conference paper at ICLR 2025
and is close to he such that
|/ hull =<(hx) + cho),
have
H ho)ll S max ( - holl
:(t)
< (ha) +1) (e(hir) + shho))
<tt) + =((ho)
c(ho)
(e(ho)) ||
hool (E(ho) +c(t))
+ <(ho))
2E(ho) + =(t)
<(t) E(hoo)
<(ho)
(e(too)
||t hool (E(ho) + +((t))
+ e(ho))
5 <(ho) + st))
|| holl
(5(to) +
+ (E(ho)
+ =t)))
|t holl
where (a) holds from the fact that e(ho) > O, so dropping one s(ho) in the denominator provides valid upper bound. Therefore, we can safely remove the absolute value function in Eqn (f) and get
hill
Hh; + R b
2 |t ho |l - |H ho)ll
|| hol
| ho)
2 t holl
mmax (H) holl
E(t)
2 ||t holl
E((ho) +1) (E(hiiz) + =hoo)
s(t) + =(ho)
||t holl
e( c(ho)
x ||t
holl - (=(ho) + +E(t))
c(hoo + +(hho)
2: + E(t)
(t) + E(ho)
-Ile holl
el e(ha)
x || holl - (=(Ho) + + + 2= + =E(t) ( c(ho)
20(ho) +e(t)
||t- holl + E(ho)
= ||t holl - (E(t) + =hho))
20 + e(t)
Finally, it is easy verify that this term is an upper bound of e (hir). since
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Published as a conference paper at ICLR 2025
| [
harl e(hix)
Hhr + R. Rll -c(hit)
(0)
2
|| holl - (e(t) + =(ho))
|| [|t- holl ll =(ho)
20(ho) + s(t)
holl (E(he) + +x(tt) (((o)))))))))))))){)))))))))((
2c(ho) + st))
(lt holl (2e(ho) + =(t))
2c Et))
- (llt holl + =(ha)())))))))) (E (ho) + +t)))
(llt holl (=(ho) + a (t))) ehho)
2hoh + =(t) (llt - holl (2e(hu) + <(t) - <(ho) - E(t))
- (=(hnn)E((ho) + =(t) - =(ho)) +(()))
0
where (a) applies the lower bound the first term, and the upper bound the second term. In conclusion, we have
[b(h)) hirll 2 e(hir,
i.e. ((hx)) & E(hrr,e(hir). This completes our proof.
B.2 MORE DISCUSSIONS ON ASMP 2.1.
Our Thm 2.3 is built upon Asmp 2.1. Informally, It assumes that the knowledge be gener- ated if representation takes some specific value. While this assumption may not hold especially in challenging scenarios (see App for more discussions), it reasonable for Thm 2.3.
Particularly, The goal of Thm 2.3 is to reveal how linearity in ReFT can inevitably hurt locality, even it appears successful in editing. Therefore, our focus is on cases where ReFT is capable of conducting the edits. The existence of such cases are confirmed by our experiments, and by its effectiveness diverse post-training tasks demonstrated in Wu et (2024). Presuming such a success, given that ReFT can only update representations, Asmp 2.1 assumes that by updating representations to some targeted (possibly unknown) value, ReFT steers output y to convey desired knowledge.
B.3 PROOF OF LEM 2.4
Lemma B.4. Let R = (r.....;r,].. r. [a],..., arl. bt diag(w (wi (h),... Wo (h)). Then BaFT
and W(h) =
(h)) h +
we we(h)ra(a) by r h).
1=1
can be expressed in matrixform
((h) = h R. W(h) (Ah R).
When using constant weighting W(h) = I, BaFT becomes to ReFT. Otherwise, rows of WR are orthonormal, making BaFT and ReFT nonequivalent.
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Table
of different methods. For basclines, only provided settings that were
different from Wang et al. (2024e).
LLaMA 2-7b(-chat)
LLaMA 3-8b-Instruct
Gemma 1.1-70-nnstret
HParams.
Value
Value
Value
FT-L
Following Wang (2024e)'s recommendation LLaMA
ROME
Following Wang et al. (2024e)'s recommendation for L.aMA 2.
MEMIT
Following Wang et al. (2024e)'s recommendation for I.L.MA 2.
AdaLoRA
Maximum Steps
Single and Continual Editing: Batched Editing
GRACE
Maximum Steps
100
250
100
Lay. to Interven
Wis
Param. Updates
Restrict the original WISE logic randomly selected 1/8 area.
Subspace Rank
12
Pos. Intervene
of Input Output
Lay. to Intervene
9:18:24:28
9:18;24:28
18:20;22:24
BaFT
Learning
Single Continual Editing:
Editing
Maximum Steps
40 for Single and Continual Editing: 70 for Batched Editing
Locality Reg. (RaPT) 0.01, 1 0.05, y 0.02
0.01, 0.1,
0.01, 0.1, 0.05
Maximum Steps
40 for Single and Continual Editing: 70 for Batched Editing
Proof. The derivations essentially come from the fact that matrix product can be expressed by sum- mation outer products. In particular, we have
((h)= h + we (h)r: (aj) - hr
h + Σ wk (h)raag
Σ
w/ wh)mrar h + Σ (h)rrrbbe
Aml
= h + R W(h)A - R (())R h + W(h)b
-R h + R. W(h) (AA bb)
h + W(h) (Ah + R),
when W(h) = I takes the identity matrix, BaFT reduces to ReFT. This completes the proof.
C IMPLEMENTATION DETAILS
We provide more implementation details about different methods.
Throughout all experiments, BaFT used a logistic regression for wx (h) for all ls [r]. ReFT was implemented as a special case of BaFT with constant weight W = I. Load balancing loss and the optional Locality regularization were removed as they were defined for weights. addition, BaFT and ReFT used the same optimizer AdamW (Loshchilov & Hutter, 2019) and learning rate. An early stopping was performed if the training loss smaller than a pre-specified threshold 0.01. We also added this early stopping encountering numeric issues when running MEMIT on Gemma, so we omitted these results.. Other prrraeparsseters reported in Tab 5.
AdALoRA after observing a similar improvement. We kept
D MORE EXPERIMENT RESULTS
D.1 COMPLETE BATCHED CONTINUAL EDITING RESULTS
Here we report the complete Batched Editing results in Tab 6 and Tab using batch size 10 and 50 respectively. The averaged result shown in Fig 3. We noted that such a batched setting makes knowledge editing resemble more conventional continual learning (Miao et al., 2021; Chen et al., 2023; Wang et al., 2024c).
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Table 6: Batched Editing performance on ZsRE dataset, evaluated after conducting T times of editing with batch size 10 sequentially, Best Avg. results bold and second best are underlined.
I
T
10
100
LLaMA 2-7b
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
MEMIT
0.89
0.84
0.97
0.90
0.87
0.82
0.93
0.87
0.04
0.04
0.02
0.03
FT-L
0.43
0.42
0.87
0.57
0.12
0.10
0.17
0.13
0.03
0.03
0.00
0.02
AdaLoRA
1.00
0.85
0.88
0.91
0.95
0,82
0.87
0.88
0.46
0.45
0.77
0.56
ReFT
0.94
0.86
0.86
0.89
0.92
0.83
0.86
0.87
0.64
0.60
0.76
0.67
BaFT (Ours)
0.93
0.84
0.95
0.91
0.92
0.83
0.95
0.90
0.59
0.55
0.98
0.71
LLaMA 3-8b-Instruct
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
MEMIT
0.91
0.85
0.96
0.91
0.84
0.78
0.87
0.83
0.06
0.06
0.03
0.05
FT-L
0.33
0.32
0.53
0.39
0.01
0,01
0.00
0.01
0.01
0.01
0.00
0.01
AdaLoRA
1.00
0.88
0.76
0.88
0.94
0.83
0.75
0.84
0.34
0.34
0.75
0.48
ReFT
0.92
0.82
0.65
0.80
0.89
0.78
0.64
00.7
0.46
0.43
0.44
00.44
BaFT (Ours)
0.92
0.82
0.83
0.86
0.90
0.78
0.85
0.84
0.43
0.40
0.95
0.59
Gemma 1.1-re-Instruet
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Avg.
FT-L
0.04
0.04
0.02
0.01
0.01
0.00
0.01
AdaLoRA
1.00
0.87
0.83
0.90
0.93
0.81
0.82
0.85
0.34
0.34
0.59
0.42
ReFT
6690
0.75"
0.86
"0.8
0.88
0.72
0.34
T.sT
6.48
0.44
0.66
T0.54
BaFT (Ours)
0.90
0.74
0.91
0.85
0.89
0.73
0.90
0.84
0.45
0.41
0.87
0.58
Table 7: Batched Editing performance on ZsRE dataset, evaluated after conducting T times of editing with batch size 50 sequentially. Best Avg. results in bold.
==1
T 10
T = 20
LLaMA 2-7b
Rel.
Gen.
Luc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
MEMIT
0.87
0.82
0.91
0.87
0.45
0.43
0.46
0.45
0.03
0.03
0.02
0.03
FT-L
0.39
0.39
0.63
0.47
0.13
0.10
0.07
0.10
0.07
0.05
0.02
0.05
AdaLoRA
1.00
0.86
0.76
0.87
0.78
0.69
0.79
0.75
0.51
0.51
0.76
0.59
ReFT
0.90
0.77
0.85
".s4
0.80
0.69
0.82
0.77
0.60
0.56
0.74
0.63
BaFT (Ours)
0.92
0.78
0.89
0.86
0.80
0.69
0.90
0.80
0.62
0.57
0.92
0.70
LLaMA 3-8b-Instruct
Rel.
Gen.
Loc.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
MEMIT
0.88
0.83
0.89
0.87
0.45
0.42
0.46
0.44
0.00
0.00
0.04
0.01
FT-L
0.32
0.29
0.42
0.34
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
AdA
1.00
0.83
0.67
0.83
0.74
0.63
0.63
0.67
0.42
0.40
0.69
0.50
ReFT
0.92
0.74
0.52
0.73
0.75
0.61
0.49
0.62
0.48
0.42
0.43
0.44
BaFT (Ours)
0.92
0.75
0.62
0.76
0.75
0.61
0.72
0.69
0.49
0.43
0.78
0.57
Gemma
Instrrctt
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
Rel.
Gen.
Loc.
Avg.
FT-L
0.02
0.02
0.01
0.02
0.00
0.00
0.00
0.01
0.00
0.00
0.00
AdaLoRA
0.13
0.08
0.02
0.08
0.08
0.06
0.02
0.05
0.03
0.03
0.00
0.02
ReFT
0.58
0.68
0.79
'6.78"
0.71
0.57
0.72
0.677
0.48
0.42
0.60
0..50
BaFT (Ours)
0.87
0.68
0.87
0.81
0.72
0.57
0.83
0.71
0.50
0.45
0.81
0.59
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Published as a conference paper at ICLR 2025
D.2 DOWNSTREAM LOCALITY PERFORMANCE
In this section we study how different editing methods affect the LLM's performance on unrelated downstream task, and evaluate how the LLM's ability of answering PIQA questions from Bisk et al. (2020) that are unrelated to the editing. The correctness measured by whether the LLM chooses the correct answer according to its perplexity. For more details we refer the readers to Yao et al. (2023).
additional measure of locality. To this end, follow Yao al. (2023)
Table 8: Downstream task (PIQA) performance after being edited with 100 Z&RE knowledge. LLM uses LLaMA-2.
Base
AdaLoRA
FT-L
ROME
MEMIT
MELO
Wig
ReFT
BaFT
PIQA Accu. 0.77
0.48
0.75
0.5
0.52
0.77
0.77
0.77
0.77
D.3 MORE DISCUSSION ON BAFT VS WISE
In experiment, note that BaFT achieves better parameter efficiency and speeds, a cost of slightly lower performance, resulting in an efficiency-effectiveeee trade-off. Notably, this efficiency of BaFT can be valuable in applications that require frequent knowledge updates.
In order to improve the effectiveness of BaFT, one possible solution is to use more parameters, given that BaFT parameter efficiency is already much higher than state-of-the-as baseline WISE. As discussed in Sec 3.3, when WISE's parameters number is reduced from WISE WISEnght its performance degrades drastically. In comparison, BaFT uses even much less parameters, but maintains highly comparable performance. Given this, we expect that using better training hyper- parameters such as learning rate to make mild performance improvement, and more parameters are needed.
To validate this, we tried to add intervention to all layers (a common practice in ReFT (Wu et al., 2024)) and increase the subspace rank to 16. This made BaFT performance editing LLaMA-2 with 100 ZsRE knowledge increased from 0.80 (Rel: 0.73, Gen: 0.68, Loc: 0.98) to 0.82 (Rel: 0.77, Gen: 0.73, Loe: 0.95). However, noted that going higher subspace rank didn't help.
Therefore, we conjecture that to build larger BaFT (and ReFT), we need to incorporate spar- sity on basis activation well. This help alleviate unintentional parameter updates in GRACE (Hartvigsen et al., 2024) and WISE (Wang et al., 2024d). In addition, such a sparsity opens the door of automating position selections: as when all bases are inactivated, BaFT makes no updates on the representation, which is equivalent to dropping the position from the fine-tuning process. We plan explore this direction in our future work.
21


Reviews:
[{"review_id": "mBwWnJyjIx", "reviewer": "Reviewer_v1Lr", "summary": "The paper discusses the challenge of updating LLMs with new knowledge without disturbing the existing knowledge they hold. Traditional methods of parameter-based updates have limitations in achieving this, as they tend to affect all inputs globally. The study introduces a new method called BaFT which achieves a better balance between making necessary updates and preserving unrelated knowledge. The effectiveness of BaFT is demonstrated through experiments on several LLMs across different editing benchmarks, showing superior performance compared to previous methods.", "questions": "Please refer to the previous section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper is well-written and easy to follow. It also provides a clear introduction of the Knowledge Editing task matter, enhancing its accessibility to a broad audience.\n\n2. The paper addresses a significant problem within the field of Knowledge Editing, focusing on a pertinent limitation associated with current methodologies. The investigation into this particular trade-off is could potentially lead to substantial advancements in the field of Knowledge Editing.\n\n3. The experimental design and execution are robust, utilizing well-chosen benchmarks to effectively demonstrate the proposed method’s validity.", "weaknesses": "1. This paper lacks visual illustrations such as diagrams or figures, which would significantly aid in the comprehension of the methods and results. I recommend the authors add figures to enhance the reader’s understanding and engagement with the content.\n\n2. There is an absence of publicly shared code in the paper or on platforms such as OpenReview. This hinders the reproducibility of the proposed method. I would suggest sharing the code through an anonymous GitHub repository or similar platform. This would greatly aid other researchers and reviewers in replicating and understanding the research.\n\n\n3. While the motivation behind the study is well-articulated, the methods deployed are somewhat conventional. This paper devotes considerable space to arguing for directly applying the ReFT method to the Knowledge Editing tasks faces limitations. However, the proposed solutions introduced by the authors do not add intriguing or novel aspects to the existing array of techniques in the field.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WE0prqMhTD", "reviewer": "Reviewer_pjBe", "summary": "Building on the ReFT, this paper introduces a novel method called Basis-level Representation Fine-tuning (BaFT) for editing knowledge within Large Language Models (LLMs) while preserving unrelated information. The authors perform a theoretical analysis that highlights the inherent limitations of existing approaches, particularly linear representation fine-tuning, which often necessitates a trade-off between editing effectiveness and the retention of unrelated knowledge (locality). To address these challenges, BaFT calculates the weight for each basis in the subspace based on the input representation, facilitating a more adaptive management of diverse knowledge types. The authors conducted experiments across three different LLMs and evaluated them against five editing benchmarks, demonstrating that BaFT surpasses existing methods in both editing performance and parameter efficiency. Ultimately, BaFT strikes a superior balance between integrating new knowledge and maintaining existing unrelated information.", "questions": "1. Should anLM in line 187 be changed to an LM?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. BaFT introduces a non-linear, input-dependent weighting mechanism for basis-level representation fine-tuning, which is a significant departure from traditional parameter-based updates.\n\n2. The authors demonstrate the effectiveness of BaFT through extensive experiments on three different LLMs and five editing benchmarks, showing superior performance in various scenarios.\n\n3. This paper provides a detailed and clear explanation of the proposed method BaFT in conjunction with the theory.", "weaknesses": "1. Innovation and Improvement Effect: The improvements of the method presented in this paper compared to ReFT are limited. Although the authors propose the ReFT-based enhancement method BaFT, experimental results indicate that these improvements show weak effectiveness in knowledge editing benchmark, thus suggesting a lack of innovation in this work.\n\n2. Questions of Method Applicability: \n- In Assumption 2.1, the authors assume as follows: \"Let text x encode s and r; text y generated by the LM will convey o if its intermediate representation takes some targeted value t.\" For instance, a sentences in the WikiData dataset: “The name of the country which Goursez Vreizh is associated with is []” is input into the model, and the generation probabilities are used to assess the accuracy of knowledge editing. Although multiple datasets are presented in this paper, these datasets exhibit high homogeneity in type. \n\n- In contrast, there are more complex benchmark datasets in the field of knowledge editing, such as MQuAKE[1] and KEBench[2]. These datasets require inputting questions into the model and determining whether the generated responses contain the corresponding answers. For example, the question from MQuAKE, “Which sport is Dudley Town F.C. associated with?” is answered with, “Dudley Town F.C. is associated with the sport of association football.” The complexity of such questions is significantly higher than that of the datasets used in this paper, making them more aligned with real-world application scenarios and challenging the authors' assumptions in Assumption 2.1. The generality of the methods in this paper has been questioned.\n\n[1] MQUAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions\n\n[2] Stable Knowledge Editing in Large Language Models\n\n3. Choice of Baselines: The baselines in this paper are relatively limited. Key literature, including LTE [1], MELO [2], StaleKE [3], and InstructEdit [4], which are fine-tuning-based methods, are not discussed or compared, resulting in a lack of comprehensiveness regarding the current field of knowledge editing.\n\n[1] Learning to edit: Aligning llms with knowledge editing. \n\n[2] Melo: Enhancing model editing with neuron-indexed dynamic lora.\n\n[3] Stable Knowledge Editing in Large Language Models\n\n[4] InstructEdit: Instruction-based Knowledge Editing for Large Language Models\n\n4. Performance of Baseline Results: The baseline results reported in the paper are significantly lower than those in existing literature, especially in the Batch Editing and Continual Editing settings, where the performance of the MEMIT method is notably inferior to previous reports. This starkly contrasts with results from several studies (e.g., [1, 2, 3]), casting doubt on the validity of this paper.\n\n[1] Model Editing at Scale leads to Gradual and Catastrophic Forgetting\n\n[2] MQUAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions\n\n[3] Learning to edit: Aligning llms with knowledge editing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "26e1aRCCc7", "reviewer": "Reviewer_rQH9", "summary": "This paper addresses the challenge of updating specific knowledge within Large Language Models (LLMs) while preserving other unrelated knowledge. Traditionally, methods have attempted to update only a small number of parameters in specific layers of LLMs, but these methods often fail to maintain the precision required for effective knowledge editing due to the inherent trade-off between editing and locality. The authors propose a novel approach called Basis-level Fine-Tuning (BaFT), which builds upon the Representation Fine-Tuning (ReFT) method. Theoretical analysis supports the introduction of BaFT, highlighting the limitations of linear representation fine-tuning and suggesting that a more precise method could improve both the specificity and locality of knowledge edits.  Instead of applying a uniform linear update across the subspace, BaFT computes a unique weight for each basis vector depending on the input representation. This allows BaFT to handle different types of knowledge adaptively, potentially resolving the inherent tension between editing performance and locality. The experiments on several models under different settings demonstrate the effectiveness of the proposed model.", "questions": "1. Some details of the method are not clear and I'm wondering when conducting ReFT or BaFT, how to decide the position for the fine-tuning? Is it the same position for different knowledge? In addition, is the experiment conducted on all layers of the specific LLM? This may be the basic information in ReFT, but for readers, it's a bit confusing.\n2. I agree that editing the knowledge in a single space would lead to the general-loc trade-off, but I think the assumption about the ball in is a bit subjective, it would be better to provide more related work here.\n3. Some typos: L187, missing blank ‘anLM’.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The prposed method obtains great performance under different knowledge editing settings including the single，continual and batch editing.\n2. The motivation is clear and reasonable and the experiments consider different kind of knowledge dataset which makes the evaluation strong.", "weaknesses": "1. There are some details missing from the main part and I have listed in the question part.\n2. The proof for the failure in the ReFT is reasonable, but why the loss used in RaFT can alleviate the problem is not clear. A proof here can make the method and theory complete and robust.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9mZTuJzvxY", "reviewer": "Reviewer_sAsi", "summary": "This paper presents BaFT, a method designed to improve knowledge editing in large language models by fine-tuning representations instead of model parameters. Unlike traditional parameter-based approaches, BaFT performs selective edits at the basis level within a learned subspace, using an input-dependent weighting mechanism. This enables precise updates to targeted knowledge while preserving unrelated knowledge. Experimental results demonstrate that BaFT excels across multiple benchmarks in single, continual, and batched editing tasks, achieving efficient knowledge updates with fewer parameters. BaFT shows strong potential for effective, dynamic knowledge editing in real-world applications.", "questions": "1.The paper asserts that it is the first work to explore an alternative selective representation-based update for knowledge editing. However, to my knowledge, REMEDI[1], as an established editing method, has already conducted preliminary investigations in this area.\n\n2.Assumption 2.2 has been proposed in GRACE[2]，authors should cite it.\n\n3.Almost editing methods have examined the ‘locality’ of these methods to downstream tasks. I recommend that the authors incorporate an evaluation of downstream tasks in the paper to ensure the reliability of the proposed editing methods.\n\n4.In the experimental section, I have concerns regarding the validity of the baseline evaluation methods. The authors did not specify the evaluation framework or the parameter choices employed. If the authors utilized the EasyEdit[3] framework to evaluate ROME and MEMIT, I suggest a unified re-evaluation of the baseline methods, as the EasyEdit evaluation framework has known mistakes.\n\n5.In the context of continual(sequential) editing, BAFT exhibits slightly inferior performance compared to WISE, which is achieved at the cost of faster training and inference speeds. How should the trade-off between these two aspects be effectively balanced?\n\n6.As an update representation for achieving editing, I believe this is not a reliable knowledge editing method compared to directly updating parameters.\n\n7.There are several expression issues in the paper, such as \"anLLM\"; however, this did not influence my overall score.\n\n[1].https://github.com/evandez/REMEDI\n\n[2].https://arxiv.org/abs/2211.11031\n\n[3].https://github.com/zjunlp/EasyEdit?tab=readme-ov-file", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.This paper introduces BaFT, which performs updates at the basis-vector level within a representation subspace, rather than relying on traditional parameter-based updates.\n\n2.The paper provides substantial theoretical and empirical support for BaFT. The theoretical section thoroughly analyzes the limitations of existing linear update methods, offering a solid rationale for BaFT’s nonlinear representation fine-tuning approach.\n\n3.BaFT offers a more efficient solution for knowledge editing in large language models, especially valuable in applications that require frequent knowledge updates.", "weaknesses": "1.Although the article presents the BaFT method and demonstrates its advantages in knowledge editing, the analysis of its limitations and potential flaws is relatively sparse.\n\n2.The experiments in the article primarily focus on a few specific benchmark datasets, which may not comprehensively reflect the performance of BaFT in various real-world applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)